Corrmotif Conc Analysis
Last updated: 2025-06-26
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Knit directory: CX5461_Project/
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📌 0.1 Micromolar
📌 Fit Limma Model Functions
## Fit limma model using code as it is found in the original cormotif code. It has
## only been modified to add names to the matrix of t values, as well as the
## limma fits
limmafit.default <- function(exprs,groupid,compid) {
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(0,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for(i in 1:compnum) {
selid1 <- which(groupid == compid[i,1])
selid2 <- which(groupid == compid[i,2])
eset <- new("ExpressionSet", exprs=cbind(exprs[,selid1],exprs[,selid2]))
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base=rep(1,(g1num+g2num)), delta=c(rep(0,g1num),rep(1,g2num)))
fit <- lmFit(eset,designmat)
fit <- eBayes(fit)
limmat[,i] <- fit$t[,2]
limmas2[i] <- fit$s2.prior
limmadf[i] <- fit$df.prior
limmav0[i] <- fit$var.prior[2]
limmag1num[i] <- g1num
limmag2num[i] <- g2num
limmafits[[i]] <- fit
# log odds
# w<-sqrt(1+fit$var.prior[2]/(1/g1num+1/g2num))
# log(0.99)+dt(fit$t[1,2],g1num+g2num-2+fit$df.prior,log=TRUE)-log(0.01)-dt(fit$t[1,2]/w, g1num+g2num-2+fit$df.prior, log=TRUE)+log(w)
}
names(limmafits) <- rownames(compid)
limmacompnum<-nrow(compid)
result<-list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
limmafit.counts <-
function (exprs, groupid, compid, norm.factor.method = "TMM", voom.normalize.method = "none")
{
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(NA,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for (i in 1:compnum) {
message(paste("Running limma for comparision",i,"/",compnum))
selid1 <- which(groupid == compid[i, 1])
selid2 <- which(groupid == compid[i, 2])
# make a new count data frame
counts <- cbind(exprs[, selid1], exprs[, selid2])
# remove NAs
not.nas <- which(apply(counts, 1, function(x) !any(is.na(x))) == TRUE)
# runn voom/limma
d <- DGEList(counts[not.nas,])
d <- calcNormFactors(d, method = norm.factor.method)
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base = rep(1, (g1num + g2num)), delta = c(rep(0,
g1num), rep(1, g2num)))
y <- voom(d, designmat, normalize.method = voom.normalize.method)
fit <- lmFit(y, designmat)
fit <- eBayes(fit)
limmafits[[i]] <- fit
limmat[not.nas, i] <- fit$t[, 2]
limmas2[i] <- fit$s2.prior
limmadf[i] <- fit$df.prior
limmav0[i] <- fit$var.prior[2]
limmag1num[i] <- g1num
limmag2num[i] <- g2num
}
limmacompnum <- nrow(compid)
names(limmafits) <- rownames(compid)
result <- list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
limmafit.list <-
function (fitlist, cmp.idx=2)
{
compnum <- length(fitlist)
genes <- c()
for (i in 1:compnum) genes <- unique(c(genes, rownames(fitlist[[i]])))
genenum <- length(genes)
limmat <- matrix(NA,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- genes
colnames(limmat) <- names(fitlist)
names(limmas2) <- names(fitlist)
names(limmadf) <- names(fitlist)
names(limmav0) <- names(fitlist)
names(limmag1num) <- names(fitlist)
names(limmag2num) <- names(fitlist)
for (i in 1:compnum) {
this.t <- fitlist[[i]]$t[,cmp.idx]
limmat[names(this.t),i] <- this.t
limmas2[i] <- fitlist[[i]]$s2.prior
limmadf[i] <- fitlist[[i]]$df.prior
limmav0[i] <- fitlist[[i]]$var.prior[cmp.idx]
limmag1num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==0)
limmag2num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==1)
}
limmacompnum <- compnum
result <- list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
## Rank genes based on statistics
generank<-function(x) {
xcol<-ncol(x)
xrow<-nrow(x)
result<-matrix(0,xrow,xcol)
z<-(1:1:xrow)
for(i in 1:xcol) {
y<-sort(x[,i],decreasing=TRUE,na.last=TRUE)
result[,i]<-match(x[,i],y)
result[,i]<-order(result[,i])
}
result
}
## Log-likelihood for moderated t under H0
modt.f0.loglike<-function(x,df) {
a<-dt(x, df, log=TRUE)
result<-as.vector(a)
flag<-which(is.na(result)==TRUE)
result[flag]<-0
result
}
## Log-likelihood for moderated t under H1
## param=c(df,g1num,g2num,v0)
modt.f1.loglike<-function(x,param) {
df<-param[1]
g1num<-param[2]
g2num<-param[3]
v0<-param[4]
w<-sqrt(1+v0/(1/g1num+1/g2num))
dt(x/w, df, log=TRUE)-log(w)
a<-dt(x/w, df, log=TRUE)-log(w)
result<-as.vector(a)
flag<-which(is.na(result)==TRUE)
result[flag]<-0
result
}
## Correlation Motif Fit
cmfit.X<-function(x, type, K=1, tol=1e-3, max.iter=100) {
## initialize
xrow <- nrow(x)
xcol <- ncol(x)
loglike0 <- list()
loglike1 <- list()
p <- rep(1, K)/K
q <- matrix(runif(K * xcol), K, xcol)
q[1, ] <- rep(0.01, xcol)
for (i in 1:xcol) {
f0 <- type[[i]][[1]]
f0param <- type[[i]][[2]]
f1 <- type[[i]][[3]]
f1param <- type[[i]][[4]]
loglike0[[i]] <- f0(x[, i], f0param)
loglike1[[i]] <- f1(x[, i], f1param)
}
condlike <- list()
for (i in 1:xcol) {
condlike[[i]] <- matrix(0, xrow, K)
}
loglike.old <- -1e+10
for (i.iter in 1:max.iter) {
if ((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations for K=",
K, sep = ""))
}
err <- tol + 1
clustlike <- matrix(0, xrow, K)
#templike <- matrix(0, xrow, 2)
templike1 <- rep(0, xrow)
templike2 <- rep(0, xrow)
for (j in 1:K) {
for (i in 1:xcol) {
templike1 <- log(q[j, i]) + loglike1[[i]]
templike2 <- log(1 - q[j, i]) + loglike0[[i]]
tempmax <- Rfast::Pmax(templike1, templike2)
templike1 <- exp(templike1 - tempmax)
templike2 <- exp(templike2 - tempmax)
tempsum <- templike1 + templike2
clustlike[, j] <- clustlike[, j] + tempmax +
log(tempsum)
condlike[[i]][, j] <- templike1/tempsum
}
clustlike[, j] <- clustlike[, j] + log(p[j])
}
#tempmax <- apply(clustlike, 1, max)
tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
for (j in 1:K) {
clustlike[, j] <- exp(clustlike[, j] - tempmax)
}
#tempsum <- apply(clustlike, 1, sum)
tempsum <- Rfast::rowsums(clustlike)
for (j in 1:K) {
clustlike[, j] <- clustlike[, j]/tempsum
}
#p.new <- (apply(clustlike, 2, sum) + 1)/(xrow + K)
p.new <- (Rfast::colsums(clustlike) + 1)/(xrow + K)
q.new <- matrix(0, K, xcol)
for (j in 1:K) {
clustpsum <- sum(clustlike[, j])
for (i in 1:xcol) {
q.new[j, i] <- (sum(clustlike[, j] * condlike[[i]][,
j]) + 1)/(clustpsum + 2)
}
}
err.p <- max(abs(p.new - p)/p)
err.q <- max(abs(q.new - q)/q)
err <- max(err.p, err.q)
loglike.new <- (sum(tempmax + log(tempsum)) + sum(log(p.new)) +
sum(log(q.new) + log(1 - q.new)))/xrow
p <- p.new
q <- q.new
loglike.old <- loglike.new
if (err < tol) {
break
}
}
clustlike <- matrix(0, xrow, K)
for (j in 1:K) {
for (i in 1:xcol) {
templike1 <- log(q[j, i]) + loglike1[[i]]
templike2 <- log(1 - q[j, i]) + loglike0[[i]]
tempmax <- Rfast::Pmax(templike1, templike2)
templike1 <- exp(templike1 - tempmax)
templike2 <- exp(templike2 - tempmax)
tempsum <- templike1 + templike2
clustlike[, j] <- clustlike[, j] + tempmax + log(tempsum)
condlike[[i]][, j] <- templike1/tempsum
}
clustlike[, j] <- clustlike[, j] + log(p[j])
}
#tempmax <- apply(clustlike, 1, max)
tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
for (j in 1:K) {
clustlike[, j] <- exp(clustlike[, j] - tempmax)
}
#tempsum <- apply(clustlike, 1, sum)
tempsum <- Rfast::rowsums(clustlike)
for (j in 1:K) {
clustlike[, j] <- clustlike[, j]/tempsum
}
p.post <- matrix(0, xrow, xcol)
for (j in 1:K) {
for (i in 1:xcol) {
p.post[, i] <- p.post[, i] + clustlike[, j] * condlike[[i]][,
j]
}
}
loglike.old <- loglike.old - (sum(log(p)) + sum(log(q) +
log(1 - q)))/xrow
loglike.old <- loglike.old * xrow
result <- list(p.post = p.post, motif.prior = p, motif.q = q,
loglike = loglike.old, clustlike=clustlike, condlike=condlike)
}
## Fit using (0,0,...,0) and (1,1,...,1)
cmfitall<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
p<-0.01
## compute loglikelihood
L0<-matrix(0,xrow,1)
L1<-matrix(0,xrow,1)
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
L0<-L0+loglike0[[i]]
L1<-L1+loglike1[[i]]
}
## EM algorithm to get MLE of p and q
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p)+L0
clustlike[,2]<-log(p)+L1
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## update motif occurrence rate
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(sum(clustlike[,2])+1)/(xrow+2)
## evaluate convergence
err<-abs(p.new-p)/p
## evaluate whether the log.likelihood increases
loglike.new<-(sum(tempmax+log(tempsum))+log(p.new)+log(1-p.new))/xrow
loglike.old<-loglike.new
p<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p)+L0
clustlike[,2]<-log(p)+L1
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post<-matrix(0,xrow,xcol)
for(i in 1:xcol) {
p.post[,i]<-clustlike[,2]
}
## return
#calculate back loglikelihood
loglike.old<-loglike.old-(log(p)+log(1-p))/xrow
loglike.old<-loglike.old*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}
## Fit each dataset separately
cmfitsep<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
p<-0.01*rep(1,xcol)
loglike.final<-rep(0,xcol)
## compute loglikelihood
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
}
p.post<-matrix(0,xrow,xcol)
## EM algorithm to get MLE of p
for(coli in 1:xcol) {
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
clustlike[,2]<-log(p[coli])+loglike1[[coli]]
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## evaluate whether the log.likelihood increases
loglike.new<-sum(tempmax+log(tempsum))/xrow
## update motif occurrence rate
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(sum(clustlike[,2]))/(xrow)
## evaluate convergence
err<-abs(p.new-p[coli])/p[coli]
loglike.old<-loglike.new
p[coli]<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
clustlike[,2]<-log(p[coli])+loglike1[[coli]]
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post[,coli]<-clustlike[,2]
loglike.final[coli]<-loglike.old
}
## return
loglike.final<-loglike.final*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.final)
}
## Fit the full model
cmfitfull<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
K<-2^xcol
p<-rep(1,K)/K
pattern<-rep(0,xcol)
patid<-matrix(0,K,xcol)
## compute loglikelihood
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
}
L<-matrix(0,xrow,K)
for(i in 1:K)
{
patid[i,]<-pattern
for(j in 1:xcol) {
if(pattern[j] < 0.5) {
L[,i]<-L[,i]+loglike0[[j]]
} else {
L[,i]<-L[,i]+loglike1[[j]]
}
}
if(i < K) {
pattern[xcol]<-pattern[xcol]+1
j<-xcol
while(pattern[j] > 1) {
pattern[j]<-0
j<-j-1
pattern[j]<-pattern[j]+1
}
}
}
## EM algorithm to get MLE of p and q
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,K)
for(j in 1:K) {
clustlike[,j]<-log(p[j])+L[,j]
}
tempmax<-apply(clustlike,1,max)
for(j in 1:K) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## update motif occurrence rate
for(j in 1:K) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(apply(clustlike,2,sum)+1)/(xrow+K)
## evaluate convergence
err<-max(abs(p.new-p)/p)
## evaluate whether the log.likelihood increases
loglike.new<-(sum(tempmax+log(tempsum))+sum(log(p.new)))/xrow
loglike.old<-loglike.new
p<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,K)
for(j in 1:K) {
clustlike[,j]<-log(p[j])+L[,j]
}
tempmax<-apply(clustlike,1,max)
for(j in 1:K) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:K) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post<-matrix(0,xrow,xcol)
for(j in 1:K) {
for(i in 1:xcol) {
if(patid[j,i] > 0.5) {
p.post[,i]<-p.post[,i]+clustlike[,j]
}
}
}
## return
#calculate back loglikelihood
loglike.old<-loglike.old-sum(log(p))/xrow
loglike.old<-loglike.old*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}
generatetype<-function(limfitted)
{
jtype<-list()
df<-limfitted$g1num+limfitted$g2num-2+limfitted$df0
for(j in 1:limfitted$compnum)
{
jtype[[j]]<-list(f0=modt.f0.loglike, f0.param=df[j], f1=modt.f1.loglike, f1.param=c(df[j],limfitted$g1num[j],limfitted$g2num[j],limfitted$v0[j]))
}
jtype
}
cormotiffit <- function(exprs, groupid=NULL, compid=NULL, K=1, tol=1e-3,
max.iter=100, BIC=TRUE, norm.factor.method="TMM",
voom.normalize.method = "none", runtype=c("logCPM","counts","limmafits"), each=3)
{
# first I want to do some typechecking. Input can be either a normalized
# matrix, a count matrix, or a list of limma fits. Dispatch the correct
# limmafit accordingly.
# todo: add some typechecking here
limfitted <- list()
if (runtype=="counts") {
limfitted <- limmafit.counts(exprs,groupid,compid, norm.factor.method, voom.normalize.method)
} else if (runtype=="logCPM") {
limfitted <- limmafit.default(exprs,groupid,compid)
} else if (runtype=="limmafits") {
limfitted <- limmafit.list(exprs)
} else {
stop("runtype must be one of 'logCPM', 'counts', or 'limmafits'")
}
jtype<-generatetype(limfitted)
fitresult<-list()
ks <- rep(K, each = each)
fitresult <- bplapply(1:length(ks), function(i, x, type, ks, tol, max.iter) {
cmfit.X(x, type, K = ks[i], tol = tol, max.iter = max.iter)
}, x=limfitted$t, type=jtype, ks=ks, tol=tol, max.iter=max.iter)
best.fitresults <- list()
for (i in 1:length(K)) {
w.k <- which(ks==K[i])
this.bic <- c()
for (j in w.k) this.bic[j] <- -2 * fitresult[[j]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
w.min <- which(this.bic == min(this.bic, na.rm = TRUE))[1]
best.fitresults[[i]] <- fitresult[[w.min]]
}
fitresult <- best.fitresults
bic <- rep(0, length(K))
aic <- rep(0, length(K))
loglike <- rep(0, length(K))
for (i in 1:length(K)) loglike[i] <- fitresult[[i]]$loglike
for (i in 1:length(K)) bic[i] <- -2 * fitresult[[i]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
for (i in 1:length(K)) aic[i] <- -2 * fitresult[[i]]$loglike + 2 * (K[i] - 1 + K[i] * limfitted$compnum)
if(BIC==TRUE) {
bestflag=which(bic==min(bic))
}
else {
bestflag=which(aic==min(aic))
}
result<-list(bestmotif=fitresult[[bestflag]],bic=cbind(K,bic),
aic=cbind(K,aic),loglike=cbind(K,loglike), allmotifs=fitresult)
}
cormotiffitall<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitall(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
cormotiffitsep<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitsep(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
cormotiffitfull<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitfull(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
plotIC<-function(fitted_cormotif)
{
oldpar<-par(mfrow=c(1,2))
plot(fitted_cormotif$bic[,1], fitted_cormotif$bic[,2], type="b",xlab="Motif Number", ylab="BIC", main="BIC")
plot(fitted_cormotif$aic[,1], fitted_cormotif$aic[,2], type="b",xlab="Motif Number", ylab="AIC", main="AIC")
}
plotMotif<-function(fitted_cormotif,title="")
{
layout(matrix(1:2,ncol=2))
u<-1:dim(fitted_cormotif$bestmotif$motif.q)[2]
v<-1:dim(fitted_cormotif$bestmotif$motif.q)[1]
image(u,v,t(fitted_cormotif$bestmotif$motif.q),
col=gray(seq(from=1,to=0,by=-0.1)),xlab="Study",yaxt = "n",
ylab="Corr. Motifs",main=paste(title,"pattern",sep=" "))
axis(2,at=1:length(v))
for(i in 1:(length(u)+1))
{
abline(v=(i-0.5))
}
for(i in 1:(length(v)+1))
{
abline(h=(i-0.5))
}
Ng=10000
if(is.null(fitted_cormotif$bestmotif$p.post)!=TRUE)
Ng=nrow(fitted_cormotif$bestmotif$p.post)
genecount=floor(fitted_cormotif$bestmotif$motif.p*Ng)
NK=nrow(fitted_cormotif$bestmotif$motif.q)
plot(0,0.7,pch=".",xlim=c(0,1.2),ylim=c(0.75,NK+0.25),
frame.plot=FALSE,axes=FALSE,xlab="No. of genes",ylab="", main=paste(title,"frequency",sep=" "))
segments(0,0.7,fitted_cormotif$bestmotif$motif.p[1],0.7)
rect(0,1:NK-0.3,fitted_cormotif$bestmotif$motif.p,1:NK+0.3,
col="dark grey")
mtext(1:NK,at=1:NK,side=2,cex=0.8)
text(fitted_cormotif$bestmotif$motif.p+0.15,1:NK,
labels=floor(fitted_cormotif$bestmotif$motif.p*Ng))
}📌 Load Required Libraries
library(Cormotif)
library(Rfast)
library(dplyr)
library(BiocParallel)
library(gprofiler2)
library(ggplot2)📌 Corrmotif Model 0.1 Micromolar
📌 Load Corrmotif Data
# Read the Corrmotif Results
Corrmotif <- read.csv("data/Corrmotif/CX5461.csv")
Corrmotif_df <- data.frame(Corrmotif)
rownames(Corrmotif_df) <- Corrmotif_df$Gene
# Filter for 0.1 Concentration Only
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("0.1", colnames(Corrmotif_df))])
# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- data.frame(groupid[, grep("0.1", colnames(groupid))])
compid <- read.csv("data/Corrmotif/Compid.csv")
compid_df <- compid[compid$Cond1 %in% unique(as.numeric(groupid_df)) & compid$Cond2 %in% unique(as.numeric(groupid_df)), ]📌 Corrmotif Model 0.1 Micromolar (K=1:8)
📌 Fit Corrmotif Model (K=1:8) (0.1 Micromolar)
set.seed(11111)
# Fit Corrmotif Model (K = 1 to 8)
set.seed(11111)
motif.fitted_0.1 <- cormotiffit(
exprs = exprs.corrmotif,
groupid = groupid_df,
compid = compid_df,
K = 1:8,
max.iter = 1000,
BIC = TRUE,
runtype = "logCPM"
)
gene_prob_0.1 <- motif.fitted_0.1$bestmotif$p.post
rownames(gene_prob_0.1) <- rownames(Corrmotif_df)
motif_prob_0.1 <- motif.fitted_0.1$bestmotif$clustlike
rownames(motif_prob_0.1) <- rownames(gene_prob_0.1)
write.csv(motif_prob_0.1,"data/cormotif_probability_genelist_0.1.csv")📌 Plot motif (0.1 Micromolar)
cormotif_0.1 <- readRDS("data/Corrmotif/cormotif_0.1.RDS")
cormotif_0.1$bic K bic
[1,] 1 291696.5
[2,] 2 284585.3
[3,] 3 283482.9
[4,] 4 283551.8
[5,] 5 283620.7
[6,] 6 283689.6
[7,] 7 283758.5
[8,] 8 283827.4plotIC(cormotif_0.1)
plotMotif(cormotif_0.1)
📌 Extract Gene Probabilities (0.1 Micromolar)
# Extract posterior probabilities for genes
gene_prob_tran_0.1 <- cormotif_0.1$bestmotif$p.post
rownames(gene_prob_tran_0.1) <- rownames(Corrmotif_df)
# Define gene probability groups
prob_1_0.1 <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] <0.5 & gene_prob_tran_0.1[,3] <0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] < 0.5 & gene_prob_tran_0.1[,6]<0.5),])
length(prob_1_0.1)[1] 12308prob_2_0.1 <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] >0.5 & gene_prob_tran_0.1[,3] >0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] > 0.5 & gene_prob_tran_0.1[,6]>0.5),])
length(prob_2_0.1)[1] 415prob_3_0.1 <- rownames(gene_prob_tran_0.1[(gene_prob_tran_0.1[,1] <0.5 & gene_prob_tran_0.1[,2] <0.5 & gene_prob_tran_0.1[,3] <0.5 & gene_prob_tran_0.1[,4] <0.5 & gene_prob_tran_0.1[,5] > 0.5 & gene_prob_tran_0.1[,6]>0.5),])
length(prob_3_0.1)[1] 1551📌 Distribution of Gene Clusters Identified by Corrmotif (0.1 micromolar)
# Load necessary library
library(ggplot2)
# Data
data <- data.frame(
Category = c("Non response (0.1 µM)", "CX-DOX mid-late response (0.1 µM)", "DOX only mid-late (0.1 µM)"),
Value = c(12308, 415, 1551)
)
# Define custom colors
custom_colors <- c("Non response (0.1 µM)" = "#FF9999",
"CX-DOX mid-late response (0.1 µM)" = "#66B2FF",
"DOX only mid-late (0.1 µM)" = "#99FF99")
# Create pie chart
ggplot(data, aes(x = "", y = Value, fill = Category)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
geom_text(aes(label = Value),
position = position_stack(vjust = 0.5),
size = 4, color = "black") +
labs(title = "Pie Chart (0.1 micromolar Corrmotif)", x = NULL, y = NULL) +
theme_void() +
scale_fill_manual(values = custom_colors)
📌 Save Corr-motif datasets for Gene Ontology analysis (0.1 Micromolar)
write.csv(data.frame(Entrez_ID = prob_1_0.1), "data/prob_1_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_2_0.1), "data/prob_2_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_3_0.1), "data/prob_3_0.1.csv", row.names = FALSE)📌 0.1 Micromolar abs logFC
# Load Required Libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
CX_0.1_3 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
DOX_0.1_3 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Compute Absolute logFC
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(absFC = abs(logFC))
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Drug = factor(Drug, levels = c("CX.5461", "DOX")),
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group,
levels = c(
"DOX only mid-late response\n(0.1 micromolar)",
"CX-DOX mid-late response\n(0.1 micromolar)",
"Non response\n(0.1 micromolar)" # Reversed Order
))
)
# **Plot the Boxplot with Faceted Labels Wrapping Correctly**
ggplot(all_toptables_0.1, aes(x = Drug, y = absFC, fill = Drug)) +
geom_boxplot() +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) + # Custom color palette
facet_grid(Response_Group ~ Timepoint, labeller = label_wrap_gen(width = 20)) + # Ensure Proper Wrapping
theme_bw() +
labs(
x = "Drugs",
y = "|Log Fold Change|",
title = "|Log Fold| for 0.1 micromolar"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.line = element_line(linewidth = 1.5),
strip.background = element_rect(fill = "gray"), # Gray background for facet labels
strip.text = element_text(size = 12, color = "black", face = "bold"), # Bold styling for facet labels
axis.text.x = element_text(size = 10, color = "black", angle = 15)
)
📌 0.1 Micromolar mean (Abs logFC) across timepoints
# Load Required Libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
CX_0.1_3 %>% mutate(Drug = "CX.5461", Timepoint = "3"),
CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "24"),
CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "48"),
DOX_0.1_3 %>% mutate(Drug = "DOX", Timepoint = "3"),
DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "24"),
DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "48")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Compute Mean Absolute logFC for Line Plot
data_summary <- all_toptables_0.1 %>%
mutate(abs_logFC = abs(logFC)) %>% # Take absolute log fold change
group_by(Response_Group, Drug, Timepoint) %>%
dplyr::summarize(mean_abs_logFC = mean(abs_logFC, na.rm = TRUE), .groups = "drop") %>%
as.data.frame()
# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
Response_Group = unique(all_toptables_0.1$Response_Group),
Drug = unique(all_toptables_0.1$Drug),
Timepoint = c("3", "24", "48")
)
# **Merge to keep missing timepoints**
data_summary <- full_join(timepoints_full, data_summary, by = c("Response_Group", "Drug", "Timepoint"))
# **Replace NA mean_abs_logFC with 0 if no genes were present**
data_summary$mean_abs_logFC[is.na(data_summary$mean_abs_logFC)] <- 0
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
mutate(
Timepoint = factor(Timepoint, levels = c("3", "24", "48"), labels = c("3 hours", "24 hours", "48 hours")),
Response_Group = factor(Response_Group, levels = c(
"DOX only mid-late response\n(0.1 micromolar)",
"CX-DOX mid-late response\n(0.1 micromolar)",
"Non response\n(0.1 micromolar)" # Reversed Order
))
)
# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")
# **Plot the Line Plot for Absolute logFC**
ggplot(data_summary, aes(x = Timepoint, y = mean_abs_logFC, group = Drug, color = Drug)) +
geom_point(size = 3) +
geom_line(size = 1.2) +
scale_color_manual(values = drug_palette) +
ylim(0, 2.5) + # Adjust the Y-axis for better visualization
facet_wrap(~ Response_Group, ncol = 1) + # Facet by Response Group (Reversed Order)
theme_bw() +
labs(
x = "Timepoints",
y = "Mean |Log Fold Change|",
title = "Mean |Log Fold Change| Across Response Groups (0.1 micromolar)",
color = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text = element_text(size = 12, color = "black"),
strip.text = element_text(size = 12, color = "black", face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 0.1 Micromolar logFC
# Load required libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# Load Datasets (Only 0.1 Micromolar)
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
# Combine All 0.1 Micromolar Datasets into a Single Dataframe
all_toptables_0.1 <- bind_rows(
CX_0.1_3 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
CX_0.1_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
CX_0.1_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
DOX_0.1_3 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
DOX_0.1_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
DOX_0.1_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.1$Entrez_ID <- as.character(all_toptables_0.1$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 micromolar)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 micromolar)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
all_toptables_0.1 <- all_toptables_0.1 %>%
mutate(
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group, levels = c(
"DOX only mid-late response\n(0.1 micromolar)",
"CX-DOX mid-late response\n(0.1 micromolar)",
"Non response\n(0.1 micromolar)" # Reversed Order
))
)
# **Plot the Boxplot**
ggplot(all_toptables_0.1, aes(x = Drug, y = logFC, fill = Drug)) +
geom_boxplot() +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
facet_grid(Response_Group ~ Timepoint) +
theme_bw() +
labs(x = "Drugs", y = "Log Fold Change", title = "Log Fold Change for 0.1 Micromolar") +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
strip.text = element_text(size = 12, face = "bold")
)
📌 0.1 Micromolar mean logFC across timepoints
# Compute Mean logFC for Line Plot
data_summary <- all_toptables_0.1 %>%
group_by(Response_Group, Drug, Timepoint) %>%
dplyr::summarize(mean_logFC = mean(logFC, na.rm = TRUE), .groups = "drop") %>%
as.data.frame()
# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
mutate(
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group, levels = c(
"DOX only mid-late response\n(0.1 micromolar)",
"CX-DOX mid-late response\n(0.1 micromolar)",
"Non response\n(0.1 micromolar)" # Reversed Order
))
)
# **Plot the Line Plot**
ggplot(data_summary, aes(x = Timepoint, y = mean_logFC, group = Drug, color = Drug)) +
geom_point(size = 3) +
geom_line(size = 1.2) +
scale_color_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
ylim(-2, 1.5) + # Adjust the Y-axis for better visualization
facet_wrap(~ Response_Group, ncol = 1) + # Facet by Response Group (Reversed Order)
theme_bw() +
labs(
x = "Timepoints",
y = "Mean Log Fold Change",
title = "Mean Log Fold Change Across Response Groups (0.1 micromolar)",
color = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text = element_text(size = 12, color = "black"),
strip.text = element_text(size = 12, color = "black", face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 Corrmotif Model 0.5 Micromolar
📌 Load Corrmotif Data
# Read the Corrmotif Results
Corrmotif <- read.csv("data/Corrmotif/CX5461.csv")
Corrmotif_df <- data.frame(Corrmotif)
rownames(Corrmotif_df) <- Corrmotif_df$Gene
# Filter for 0.5 Concentration Only
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("0.5", colnames(Corrmotif_df))])
# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- data.frame(groupid[, grep("0.5", colnames(groupid))])
compid <- read.csv("data/Corrmotif/Compid.csv")
compid_df <- compid[compid$Cond1 %in% unique(as.numeric(groupid_df)) & compid$Cond2 %in% unique(as.numeric(groupid_df)), ]📌 Corrmotif Model 0.5 Micromolar (K=1:8)
📌 Fit Corrmotif Model (K=1:8) (0.5 Micromolar)
# Fit Corrmotif Model (K = 1 to 8)
set.seed(11111)
motif.fitted_0.5 <- cormotiffit(
exprs = exprs.corrmotif,
groupid = groupid_df,
compid = compid_df,
K = 1:8,
max.iter = 1000,
BIC = TRUE,
runtype = "logCPM"
)
gene_prob_0.5 <- motif.fitted_0.5$bestmotif$p.post
rownames(gene_prob_0.5) <- rownames(Corrmotif_df)
motif_prob_0.5 <- motif.fitted_0.5$bestmotif$clustlike
rownames(motif_prob_0.5) <- rownames(gene_prob_0.5)
write.csv(motif_prob_0.5,"data/cormotif_probability_genelist_0.5.csv")📌 Plot motif (0.5 Micromolar)
cormotif_0.5 <- readRDS("data/Corrmotif/cormotif_0.5.RDS")
cormotif_0.5$bic K bic
[1,] 1 352140.7
[2,] 2 346785.8
[3,] 3 344812.9
[4,] 4 344860.1
[5,] 5 344751.9
[6,] 6 344820.8
[7,] 7 344889.7
[8,] 8 344966.6plotIC(cormotif_0.5)
plotMotif(cormotif_0.5)
📌 Extract Gene Probabilities (0.5 Micromolar)
# Extract posterior probabilities for genes
gene_prob_tran_0.5 <- cormotif_0.5$bestmotif$p.post
rownames(gene_prob_tran_0.5) <- rownames(Corrmotif_df)
# Define gene probability groups
prob_1_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] < 0.5 & gene_prob_tran_0.5[,6]<0.5),])
length(prob_1_0.5)[1] 7134prob_2_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] >0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>=0.02),])
length(prob_2_0.5)[1] 179prob_3_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] <0.5 & gene_prob_tran_0.5[,3] <0.5 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>0.5),])
length(prob_3_0.5)[1] 6450prob_4_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] >= 0.1 & gene_prob_tran_0.5[,2] > 0.5 & gene_prob_tran_0.5[,3] > 0.5 & gene_prob_tran_0.5[,4] >= 0.02 & gene_prob_tran_0.5[,5] < 0.5 & gene_prob_tran_0.5[,6] < 0.5),])
length(prob_4_0.5)[1] 142prob_5_0.5 <- rownames(gene_prob_tran_0.5[(gene_prob_tran_0.5[,1] <0.5 & gene_prob_tran_0.5[,2] >0.5 & gene_prob_tran_0.5[,3] >0.5 & gene_prob_tran_0.5[,4] >=0.02 & gene_prob_tran_0.5[,4] <0.5 & gene_prob_tran_0.5[,5] > 0.5 & gene_prob_tran_0.5[,6]>0.5),])
length(prob_5_0.5)[1] 221📌 Distribution of Gene Clusters Identified by Corrmotif (0.5 micromolar)
# Load necessary library
library(ggplot2)
# Data
data <- data.frame(
Category = c("Non response (0.5)", "DOX-specific response (0.5 µM)", "DOX only mid-late response (0.5 µM)", "CX + DOX (early) response (0.5 µM)", "DOX + CX (mid-late) response (0.5 µM)"),
Value = c(7134,179,6450,142,221)
)
# Add values to category names (to be displayed in the legend)
data$Category <- paste0(data$Category, " (", data$Value, ")")
# Define custom colors with updated category names
custom_colors <- setNames(
c("#FF9999", "#FF66CC", "#66B2FF", "#99FF99", "#FFD700"),
data$Category # Ensures color names match updated categories
)
# Create pie chart without number labels inside
ggplot(data, aes(x = "", y = Value, fill = Category)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
labs(title = "Pie Chart (0.5 micromolar Corrmotif)", x = NULL, y = NULL) +
theme_void() +
scale_fill_manual(values = custom_colors)
📌 Combined pie charts for concentrations
# Load necessary libraries
library(ggplot2)
library(dplyr)
# Data for 0.1 µM
data_0.1 <- data.frame(
Category = c("Non response", "CX-DOX mid-late response", "DOX only mid-late"),
Value = c(12308, 415, 1551),
Concentration = "0.1 µM"
)
# Data for 0.5 µM
data_0.5 <- data.frame(
Category = c("Non response", "DOX specific response", "DOX only mid-late response",
"CX + DOX (early) response", "DOX + CX (mid-late) response"),
Value = c(7134, 179, 6450, 142, 221),
Concentration = "0.5 µM"
)
# Combine both datasets
combined_data <- bind_rows(data_0.1, data_0.5)
# Add values to category names (for legend only)
combined_data$Category_Legend <- paste0(combined_data$Category, " (", combined_data$Value, ")")
# Define custom colors for updated categories
custom_colors <- c(
"Non response (12308)" = "#FF9999",
"CX-DOX mid-late response (415)" = "#66B2FF",
"DOX only mid-late (1551)" = "#99FF99",
"Non response (7134)" = "#FF9999",
"DOX specific response (179)" = "#FF66CC",
"DOX only mid-late response (6450)" = "#99FF99",
"CX + DOX (early) response (142)" = "#FFD700",
"DOX + CX (mid-late) response (221)" = "#8A2BE2"
)
# Ensure categories appear in a specific order
combined_data$Category_Legend <- factor(combined_data$Category_Legend, levels = names(custom_colors))
# Create faceted pie charts without numbers inside the slices
pie_chart <- ggplot(combined_data, aes(x = "", y = Value, fill = Category_Legend)) +
geom_bar(width = 1, stat = "identity") +
coord_polar("y", start = 0) +
facet_wrap(~ Concentration) + # Facet by concentration (side-by-side)
labs(title = "Corrmotif Pie Charts for 0.1 µM and 0.5 µM", x = NULL, y = NULL, fill = "Category") +
theme_void() +
theme(
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.5), # Adds box around facets
strip.background = element_rect(fill = "white", color = "black", linewidth = 1), # Box for facet titles
strip.text = element_text(size = 12, face = "bold", color = "black"),
legend.title = element_text(size = 12, face = "bold"), # Bold legend title
legend.text = element_text(size = 10) # Adjust legend text size
) +
scale_fill_manual(values = custom_colors)
# Display the plot
print(pie_chart)
📌 Save Corr-motif datasets for Gene Ontology analysis (0.5 Micromolar)
write.csv(data.frame(Entrez_ID = prob_1_0.5), "data/prob_1_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_2_0.5), "data/prob_2_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_3_0.5), "data/prob_3_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_4_0.5), "data/prob_4_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_5_0.5), "data/prob_5_0.5.csv", row.names = FALSE)📌 0.5 Micromolar abs logFC
# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Convert datasets to DataFrames
Toptable_CX_0.5_3_df <- data.frame(CX_0.5_3)
Toptable_CX_0.5_24_df <- data.frame(CX_0.5_24)
Toptable_CX_0.5_48_df <- data.frame(CX_0.5_48)
Toptable_DOX_0.5_3_df <- data.frame(DOX_0.5_3)
Toptable_DOX_0.5_24_df <- data.frame(DOX_0.5_24)
Toptable_DOX_0.5_48_df <- data.frame(DOX_0.5_48)
# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
Toptable_CX_0.5_3_df %>% mutate(Drug = "CX.5461", Timepoint = "3"),
Toptable_CX_0.5_24_df %>% mutate(Drug = "CX.5461", Timepoint = "24"),
Toptable_CX_0.5_48_df %>% mutate(Drug = "CX.5461", Timepoint = "48"),
Toptable_DOX_0.5_3_df %>% mutate(Drug = "DOX", Timepoint = "3"),
Toptable_DOX_0.5_24_df %>% mutate(Drug = "DOX", Timepoint = "24"),
Toptable_DOX_0.5_48_df %>% mutate(Drug = "DOX", Timepoint = "48")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response\n(0.5 micromolar)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early) response\n(0.5 micromolar)",
Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late) response\n(0.5 micromolar)",
TRUE ~ NA_character_
)
)
# Remove NA Values (Genes Not in Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>% filter(!is.na(Response_Group))
# Compute Absolute logFC
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(absFC = abs(logFC))
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Drug = factor(Drug, levels = c("CX.5461", "DOX")),
Timepoint = factor(Timepoint, levels = c("3", "24", "48"),
labels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group,
levels = c("DOX + CX (mid-late) response\n(0.5 micromolar)",
"CX + DOX (early) response\n(0.5 micromolar)",
"DOX only mid-late response\n(0.5 micromolar)",
"DOX-specific response\n(0.5 micromolar)",
"Non response\n(0.5 micromolar)")) # Reversed Order
)
# **Plot the Boxplot with Faceted Labels Wrapping Correctly**
ggplot(all_toptables_0.5, aes(x = Drug, y = absFC, fill = Drug)) +
geom_boxplot() +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) + # Custom color palette
facet_grid(Response_Group ~ Timepoint, labeller = label_wrap_gen(width = 20)) + # Ensure Proper Wrapping
theme_bw() +
labs(
x = "Drugs",
y = "|Log Fold Change|",
title = "|Log Fold| for 0.5 micromolar"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.line = element_line(linewidth = 1.5),
strip.background = element_rect(fill = "gray"), # Gray background for facet labels
strip.text = element_text(size = 12, color = "black", face = "bold"), # Bold styling for facet labels
axis.text.x = element_text(size = 10, color = "black", angle = 15)
)
📌 0.5 Micromolar mean (Abs logFC) across timepoints
# Load required libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
CX_0.5_3 %>% mutate(Drug = "CX.5461", Timepoint = "3"),
CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "24"),
CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "48"),
DOX_0.5_3 %>% mutate(Drug = "DOX", Timepoint = "3"),
DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "24"),
DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "48")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response\n(0.5 micromolar)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early) response\n(0.5 micromolar)",
Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late) response\n(0.5 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) # Remove NA values
# Compute Mean Absolute logFC for Line Plot
data_summary <- all_toptables_0.5 %>%
mutate(abs_logFC = abs(logFC)) %>%
group_by(Response_Group, Drug, Timepoint) %>%
dplyr::summarize(mean_abs_logFC = mean(abs_logFC, na.rm = TRUE), .groups = "drop") %>%
as.data.frame()
# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
Response_Group = unique(all_toptables_0.5$Response_Group),
Drug = unique(all_toptables_0.5$Drug),
Timepoint = c("3", "24", "48")
)
# **Merge to keep missing timepoints**
data_summary <- full_join(timepoints_full, data_summary, by = c("Response_Group", "Drug", "Timepoint"))
# **Replace NA mean_abs_logFC with 0 if no genes were present**
data_summary$mean_abs_logFC[is.na(data_summary$mean_abs_logFC)] <- 0
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary <- data_summary %>%
mutate(
Timepoint = factor(Timepoint, levels = c("3", "24", "48"), labels = c("3 hours", "24 hours", "48 hours")),
Response_Group = factor(Response_Group, levels = c(
"DOX + CX (mid-late) response\n(0.5 micromolar)",
"CX + DOX (early) response\n(0.5 micromolar)",
"DOX only mid-late response\n(0.5 micromolar)",
"DOX-specific response\n(0.5 micromolar)",
"Non response\n(0.5 micromolar)" # Reversed order
))
)
# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")
# **Plot the Line Plot for Mean Absolute logFC**
ggplot(data_summary, aes(x = Timepoint, y = mean_abs_logFC, group = Drug, color = Drug)) +
geom_point(size = 3) +
geom_line(size = 1.2) +
scale_color_manual(values = drug_palette) +
ylim(0, 2.5) + # Adjust the Y-axis for better visualization
facet_wrap(~ Response_Group, ncol = 1) + # Facet by Response Group (Reversed Order)
theme_bw() +
labs(
x = "Timepoints",
y = "Mean |Log Fold Change|",
title = "Mean |Log Fold Change| Across Response Groups (0.5 micromolar)",
color = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text = element_text(size = 12, color = "black"),
strip.text = element_text(size = 12, color = "black", face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 0.5 Micromolar logFC
# Load required libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
CX_0.5_3 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
DOX_0.5_3 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)
# Assign Response Groups
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response\n(0.5 micromolar)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early) response\n(0.5 micromolar)",
Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late) response\n(0.5 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Convert factors to ensure correct ordering (Reversed Order for Response Groups)
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group, levels = c(
"DOX + CX (mid-late) response\n(0.5 micromolar)",
"CX + DOX (early) response\n(0.5 micromolar)",
"DOX only mid-late response\n(0.5 micromolar)",
"DOX-specific response\n(0.5 micromolar)",
"Non response\n(0.5 micromolar)" # Reversed Order
))
)
# **Plot the Boxplot**
ggplot(all_toptables_0.5, aes(x = Drug, y = logFC, fill = Drug)) +
geom_boxplot() +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
facet_grid(Response_Group ~ Timepoint) +
theme_bw() +
labs(x = "Drugs", y = "Log Fold Change", title = "Log Fold Change for 0.5 Micromolar") +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
strip.text = element_text(size = 12, face = "bold")
)
📌 0.5 Micromolar mean logFC across timepoints
# Load Required Libraries
library(dplyr)
library(ggplot2)
# Load Response Groups from CSV Files
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load Datasets (Only 0.5 Micromolar)
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Combine All 0.5 Micromolar Datasets into a Single Dataframe
all_toptables_0.5 <- bind_rows(
CX_0.5_3 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 3 hours"),
CX_0.5_24 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 24 hours"),
CX_0.5_48 %>% mutate(Drug = "CX.5461", Timepoint = "Timepoint: 48 hours"),
DOX_0.5_3 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 3 hours"),
DOX_0.5_24 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 24 hours"),
DOX_0.5_48 %>% mutate(Drug = "DOX", Timepoint = "Timepoint: 48 hours")
)
# Convert `Entrez_ID` to Character to Avoid `%in%` Issues
all_toptables_0.5$Entrez_ID <- as.character(all_toptables_0.5$Entrez_ID)
# Assign Response Groups with Line Breaks for Better Plotting
all_toptables_0.5 <- all_toptables_0.5 %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 micromolar)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response\n(0.5 micromolar)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response\n(0.5 micromolar)",
Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early) response\n(0.5 micromolar)",
Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late) response\n(0.5 micromolar)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# Compute Mean logFC for Line Plot
data_summary_0.5 <- all_toptables_0.5 %>%
group_by(Response_Group, Drug, Timepoint) %>%
dplyr::summarize(mean_logFC = mean(logFC, na.rm = TRUE), .groups = "drop") %>%
as.data.frame()
# **Ensure all timepoints exist in the summary**
timepoints_full <- expand.grid(
Response_Group = unique(all_toptables_0.5$Response_Group),
Drug = unique(all_toptables_0.5$Drug),
Timepoint = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")
)
# **Merge to keep missing timepoints**
data_summary_0.5 <- full_join(timepoints_full, data_summary_0.5, by = c("Response_Group", "Drug", "Timepoint"))
# **Replace NA mean_logFC with 0 if no genes were present**
data_summary_0.5$mean_logFC[is.na(data_summary_0.5$mean_logFC)] <- 0
# Convert Factors for Proper Ordering (Reversed Order for Response Groups)
data_summary_0.5 <- data_summary_0.5 %>%
mutate(
Timepoint = factor(Timepoint, levels = c("Timepoint: 3 hours", "Timepoint: 24 hours", "Timepoint: 48 hours")),
Response_Group = factor(Response_Group, levels = c(
"DOX + CX (mid-late) response\n(0.5 micromolar)",
"CX + DOX (early) response\n(0.5 micromolar)",
"DOX only mid-late response\n(0.5 micromolar)",
"DOX-specific response\n(0.5 micromolar)",
"Non response\n(0.5 micromolar)" # Reversed Order
))
)
# Define custom drug palette
drug_palette <- c("CX.5461" = "blue", "DOX" = "red")
# **Plot the Line Plot for Mean logFC**
ggplot(data_summary_0.5, aes(x = Timepoint, y = mean_logFC, group = Drug, color = Drug)) +
geom_point(size = 3) +
geom_line(size = 1.2) +
scale_color_manual(values = drug_palette) +
ylim(-2, 1.5) + # Adjust the Y-axis for better visualization
facet_wrap(~ Response_Group, ncol = 1) + # Facet by Response Group (Reversed Order)
theme_bw() +
labs(
x = "Timepoints",
y = "Mean Log Fold Change",
title = "Mean Log Fold Change Across Response Groups (0.5 micromolar)",
color = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text = element_text(size = 12, color = "black"),
strip.text = element_text(size = 12, color = "black", face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 Proportion of DNA Damage Repair Genes (0.1 micromolar) (MD Anderson dataset)
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)Warning: package 'tidyr' was built under R version 4.3.3library(org.Hs.eg.db)Warning: package 'AnnotationDbi' was built under R version 4.3.2Warning: package 'IRanges' was built under R version 4.3.1Warning: package 'S4Vectors' was built under R version 4.3.2# **🔹 Read DNA Damage Repair Gene List**
DNA_damage <- read.csv("data/DNA_Damage.csv", stringsAsFactors = FALSE)
# Convert gene symbols to Entrez IDs
DNA_damage <- DNA_damage %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = DNA_damage$Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))
DNA_damage_genes <- na.omit(DNA_damage$Entrez_ID)
# **🔹 Load Corrmotif Groups for 0.1 Concentration**
prob_groups_0.1 <- list(
"Non Response (0.1)" = read.csv("data/prob_1_0.1.csv")$Entrez_ID,
"CX_DOX mid-late (0.1)" = read.csv("data/prob_2_0.1.csv")$Entrez_ID,
"DOX only mid-late (0.1)"= read.csv("data/prob_3_0.1.csv")$Entrez_ID
)
# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.1 <- bind_rows(
lapply(prob_groups_0.1, function(ids) {
data.frame(Entrez_ID = ids)
}),
.id = "Response_Group"
)
# **🔹 Match Entrez_IDs with DNA Damage Repair Genes**
corrmotif_df_0.1 <- corrmotif_df_0.1 %>%
mutate(DNA_Damage = ifelse(Entrez_ID %in% DNA_damage_genes, "Yes", "No"))
# **🔹 Count DNA Damage Repair Genes in Each Response Group**
proportion_data <- corrmotif_df_0.1 %>%
group_by(Response_Group, DNA_Damage) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Response_Group) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$DNA_Damage <- factor(proportion_data$DNA_Damage, levels = c("Yes", "No"))
# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.1)", "CX_DOX mid-late (0.1)","DOX only mid-late (0.1)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)
# **🔹 Perform Chi-Square Tests for "DOX only mid-late (0.1)" and "CX_DOX mid-late (0.1)" vs "Non Response (0.1)"**
non_response_counts <- proportion_data %>%
filter(Response_Group == "Non Response (0.1)") %>%
dplyr::select(DNA_Damage, Count) %>%
{setNames(.$Count, .$DNA_Damage)} # Convert to named vector
chi_results <- proportion_data %>%
filter(Response_Group %in% c("CX_DOX mid-late (0.1)","DOX only mid-late (0.1)")) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[DNA_Damage %in% c("Yes", "No")]
if (!"Yes" %in% DNA_Damage) group_counts <- c(group_counts, 0)
if (!"No" %in% DNA_Damage) group_counts <- c(0, group_counts)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
non_response_counts["Yes"], non_response_counts["No"]
), nrow = 2, byrow = TRUE)
# Perform chi-square test if all values are valid
if (all(contingency_table >= 0 & is.finite(contingency_table))) {
chisq.test(contingency_table)$p.value
} else {
NA
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")
# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
Response_Group = c("CX_DOX mid-late (0.1)", "DOX only mid-late (0.1)"),
y_pos = 105, # Fixed at 105% of Y-axis
Significance = chi_results$Significance
)
# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = DNA_Damage)) +
geom_bar(stat = "identity", position = "stack") + # Stacked bars
geom_text(
data = star_positions,
aes(x = Response_Group, y = y_pos, label = Significance), # Place stars at fixed 105%
inherit.aes = FALSE,
size = 6, color = "black", fontface = "bold", vjust = 0 # Keeps stars aligned
) +
scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) + # Fixed Y-axis to 100%
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) + # Yes (Red), No (Blue)
labs(
title = "Proportion of DNA Damage Repair Genes in\n0.1 Corrmotif Response Groups",
x = "Response Groups (0.1 Concentration)",
y = "Percentage",
fill = "DNA Damage Repair"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
strip.background = element_blank(),
strip.text = element_text(size = 12, face = "bold")
)
📌 Proportion of DNA Damage Repair Genes (0.5 micromolar) (MD Anderson dataset)
# Load necessary libraries
library(dplyr)
library(ggplot2)
library(tidyr)
library(org.Hs.eg.db)
# **🔹 Read DNA Damage Repair Gene List**
DNA_damage <- read.csv("data/DNA_Damage.csv", stringsAsFactors = FALSE)
# Convert gene symbols to Entrez IDs
DNA_damage <- DNA_damage %>%
mutate(Entrez_ID = mapIds(org.Hs.eg.db,
keys = DNA_damage$Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"))
DNA_damage_genes <- na.omit(DNA_damage$Entrez_ID)
# **🔹 Load Corrmotif Groups for 0.5 Concentration**
prob_groups_0.5 <- list(
"Non Response (0.5)" = read.csv("data/prob_1_0.5.csv")$Entrez_ID,
"DOX-specific response (0.5)" = read.csv("data/prob_2_0.5.csv")$Entrez_ID,
"DOX only mid-late response (0.5)" = read.csv("data/prob_3_0.5.csv")$Entrez_ID,
"CX DOX (early) response (0.5)" = read.csv("data/prob_4_0.5.csv")$Entrez_ID,
"DOX + CX (mid-late) response (0.5)" = read.csv("data/prob_5_0.5.csv")$Entrez_ID
)
# **🔹 Create Dataframe for Corrmotif Groups**
corrmotif_df_0.5 <- bind_rows(
lapply(prob_groups_0.5, function(ids) {
data.frame(Entrez_ID = ids)
}),
.id = "Response_Group"
)
# **🔹 Match Entrez_IDs with DNA Damage Repair Genes**
corrmotif_df_0.5 <- corrmotif_df_0.5 %>%
mutate(DNA_Damage = ifelse(Entrez_ID %in% DNA_damage_genes, "Yes", "No"))
# **🔹 Count DNA Damage Repair Genes in Each Response Group**
proportion_data <- corrmotif_df_0.5 %>%
group_by(Response_Group, DNA_Damage) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Response_Group) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# **🔹 Ensure "Yes" is at the Bottom and "No" at the Top**
proportion_data$DNA_Damage <- factor(proportion_data$DNA_Damage, levels = c("Yes", "No"))
# **🔹 Set Order of Response Groups for X-axis**
response_order <- c("Non Response (0.5)", "DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = response_order)
# **🔹 Perform Chi-Square Tests for Each Response Group vs Non-Response**
non_response_counts <- proportion_data %>%
filter(Response_Group == "Non Response (0.5)") %>%
dplyr::select(DNA_Damage, Count) %>%
{setNames(.$Count, .$DNA_Damage)} # Convert to named vector
# **Comparing Each Group Against "Non Response (0.5)"**
chi_results <- proportion_data %>%
filter(Response_Group %in% c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)")) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[DNA_Damage %in% c("Yes", "No")]
if (!"Yes" %in% DNA_Damage) group_counts <- c(group_counts, 0)
if (!"No" %in% DNA_Damage) group_counts <- c(0, group_counts)
contingency_table <- matrix(c(
group_counts[1], group_counts[2], # Response group counts
non_response_counts["Yes"], non_response_counts["No"] # Non-response counts
), nrow = 2, byrow = TRUE)
# Perform chi-square test if all values are valid
if (all(contingency_table >= 0 & is.finite(contingency_table))) {
chisq.test(contingency_table)$p.value
} else {
NA
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
# **🔹 Merge Chi-Square Results into Proportion Data**
proportion_data <- proportion_data %>%
left_join(chi_results %>% dplyr::select(Response_Group, Significance), by = "Response_Group")
# **🔹 Set Star Position Uniform Across Groups at 105%**
star_positions <- data.frame(
Response_Group = c("DOX-specific response (0.5)", "DOX only mid-late response (0.5)",
"CX DOX (early) response (0.5)", "DOX + CX (mid-late) response (0.5)"),
y_pos = 105, # Fixed at 105% of Y-axis
Significance = chi_results$Significance
)
# **🔹 Generate Proportion Plot with Chi-Square Stars**
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = DNA_Damage)) +
geom_bar(stat = "identity", position = "stack") + # Stacked bars
geom_text(
data = star_positions,
aes(x = Response_Group, y = y_pos, label = Significance), # Place stars at fixed 105%
inherit.aes = FALSE,
size = 6, color = "black", fontface = "bold", vjust = 0 # Keeps stars aligned
) +
scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 110)) + # **Y-axis now limited to 110% for visibility**
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) + # Yes (Red), No (Blue)
labs(
title = "Proportion of DNA Damage Repair Genes in\n0.5 Corrmotif Response Groups",
x = "Response Groups (0.5 Concentration)",
y = "Percentage",
fill = "DNA Damage Repair Genes"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
strip.background = element_blank(),
strip.text = element_text(size = 12, face = "bold")
)
📌 Proportion of DNA Damage Repair Genes (Combined) (MD Anderson dataset)
# Load Required Libraries
library(dplyr)
library(ggplot2)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load DNA Damage Genes -----------------
DNA_damage <- read.csv("data/DNA_Damage.csv", stringsAsFactors = FALSE)
DNA_damage$Entrez_ID <- mapIds(
org.Hs.eg.db,
keys = DNA_damage$Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
dna_damage_ids <- na.omit(DNA_damage$Entrez_ID)
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% dna_damage_ids, "Yes", "No"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% dna_damage_ids, "Yes", "No"),
Concentration = "0.5 µM"
)
# ----------------- Combine All -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Set Order: "Yes" at bottom -----------------
proportion_data$Category <- factor(proportion_data$Category, levels = c("Yes", "No"))
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
dplyr::select(Category, Count) %>%
{setNames(.$Count, .$Category)}
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("Yes", "No")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["Yes"], ref_counts["No"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Perform tests
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")
chi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")
chi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
proportion_data <- proportion_data %>%
left_join(chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Star Positions -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(Significance == "*") %>%
mutate(y_pos = 105)
# ----------------- Stack Order -----------------
proportion_data <- proportion_data %>%
arrange(Concentration, Response_Group, Category)
# ----------------- Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5, color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "DNA Damage Gene Proportions Across\nCormotif Clusters (0.1 and 0.5 µM)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "DNA Damage Gene"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
📌 Proportion of DNA Damage Response Genes (Molecular signature dataset)
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DDR Entrez IDs -----------------
ddr_entrez_ids <- as.character(c(
10111, 1017, 1019, 1020, 1021, 1026, 1027, 10912, 11011, 1111,
11200, 1385, 1643, 1647, 1869, 207, 2177, 25, 27113, 27244,
3014, 317, 355, 4193, 4292, 4361, 4609, 4616, 4683, 472, 50484,
5366, 5371, 54205, 545, 55367, 5591, 581, 5810, 5883, 5884,
5888, 5893, 5925, 595, 5981, 6118, 637, 672, 7157, 7799,
8243, 836, 841, 84126, 842, 8795, 891, 894, 896, 898,
9133, 9134, 983, 9874, 993, 995, 5916
))
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% ddr_entrez_ids, "DDR Genes", "Non-DDR Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% ddr_entrez_ids, "DDR Genes", "Non-DDR Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("DDR Genes", "Non-DDR Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["DDR Genes"], ref_counts["Non-DDR Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Run test for each concentration
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")Warning: There was 1 warning in `summarise()`.
ℹ In argument: `p_value = { ... }`.
ℹ In group 1: `Response_Group = "CX-DOX mid-late response"`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrectchi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")Warning: There were 2 warnings in `summarise()`.
The first warning was:
ℹ In argument: `p_value = { ... }`.
ℹ In group 1: `Response_Group = "CX total + DOX early response"`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrect
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.chi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
# Merge test results into proportions
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Reorder Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Star Label Position -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5,
color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c(
"DDR Genes" = "#E41A1C",
"Non-DDR Genes" = "#377EB8"
)) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "DNA Damage Response Gene Proportions Across\nCormotif Clusters (0.1 and 0.5)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of DNA damage response genes across corrmotifs (Molecular signature dataset) (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)Warning: package 'tidyverse' was built under R version 4.3.2Warning: package 'readr' was built under R version 4.3.3Warning: package 'purrr' was built under R version 4.3.3Warning: package 'stringr' was built under R version 4.3.2Warning: package 'lubridate' was built under R version 4.3.3library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DDR Entrez IDs -----------------
entrez_ids <- c(
10111, 1017, 1019, 1020, 1021, 1026, 1027, 10912, 11011, 1111,
11200, 1385, 1643, 1647, 1869, 207, 2177, 25, 27113, 27244,
3014, 317, 355, 4193, 4292, 4361, 4609, 4616, 4683, 472, 50484,
5366, 5371, 54205, 545, 55367, 5591, 581, 5810, 5883, 5884,
5888, 5893, 5925, 595, 5981, 6118, 637, 672, 7157, 7799,
8243, 836, 841, 84126, 842, 8795, 891, 894, 896, 898,
9133, 9134, 983, 9874, 993, 995, 5916
)
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% as.character(entrez_ids)) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% as.character(entrez_ids)) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM",
TRUE ~ NA_character_
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(
logFC = mean(logFC, na.rm = TRUE),
.groups = "drop"
)
# ----------------- Merge with CorMotif DDR Genes -----------------
ddr_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
ddr_logfc$Response_Group <- factor(ddr_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
star_df <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- ddr_logfc$logFC[ddr_logfc$Group == resp_group]
control_vals <- ddr_logfc$logFC[ddr_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
if (pval < 0.05) {
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
TRUE ~ "*"
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(Group = resp_group, y_pos = y_pos, label = label, P_Value = signif(pval, 4)))
}
}
return(NULL)
}) %>% bind_rows()
# ----------------- Prepare Annotation -----------------
label_data <- star_df %>%
separate(Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(ddr_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of DDR Genes\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of DNA damage response genes across corrmotifs (Molecular signature dataset) (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DDR Entrez IDs -----------------
entrez_ids <- c(
10111, 1017, 1019, 1020, 1021, 1026, 1027, 10912, 11011, 1111,
11200, 1385, 1643, 1647, 1869, 207, 2177, 25, 27113, 27244,
3014, 317, 355, 4193, 4292, 4361, 4609, 4616, 4683, 472, 50484,
5366, 5371, 54205, 545, 55367, 5591, 581, 5810, 5883, 5884,
5888, 5893, 5925, 595, 5981, 6118, 637, 672, 7157, 7799,
8243, 836, 841, 84126, 842, 8795, 891, 894, 896, 898,
9133, 9134, 983, 9874, 993, 995, 5916
) %>% as.character()
# ----------------- Load CorMotif Groups -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with CorMotif DDR Genes -----------------
ddr_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(ddr_all$Response_Group)[-1] # exclude "Non response"
stat_results <- list()
for (conc in unique(ddr_all$Concentration)) {
for (tp in levels(ddr_all$Timepoint)) {
sub <- ddr_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(ddr_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(ddr_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "DDR Gene logFC by CorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of DNA damage response genes across corrmotifs (Molecular signature dataset) (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DDR Entrez IDs -----------------
ddr_ids <- c(
10111, 1017, 1019, 1020, 1021, 1026, 1027, 10912, 11011, 1111,
11200, 1385, 1643, 1647, 1869, 207, 2177, 25, 27113, 27244,
3014, 317, 355, 4193, 4292, 4361, 4609, 4616, 4683, 472, 50484,
5366, 5371, 54205, 545, 55367, 5591, 581, 5810, 5883, 5884,
5888, 5893, 5925, 595, 5981, 6118, 637, 672, 7157, 7799,
8243, 836, 841, 84126, 842, 8795, 891, 894, 896, 898,
9133, 9134, 983, 9874, 993, 995, 5916
) %>% as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% ddr_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM DDR) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "DDR Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
################### DDR Genes — 0.5 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DDR Entrez IDs -----------------
ddr_ids <- c(
10111, 1017, 1019, 1020, 1021, 1026, 1027, 10912, 11011, 1111,
11200, 1385, 1643, 1647, 1869, 207, 2177, 25, 27113, 27244,
3014, 317, 355, 4193, 4292, 4361, 4609, 4616, 4683, 472, 50484,
5366, 5371, 54205, 545, 55367, 5591, 581, 5810, 5883, 5884,
5888, 5893, 5925, 595, 5981, 6118, 637, 672, 7157, 7799,
8243, 836, 841, 84126, 842, 8795, 891, 894, 896, 898,
9133, 9134, 983, 9874, 993, 995, 5916
) %>% as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% ddr_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM DDR) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "DDR Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Proportion of P53 target Genes across corrmotif clusters
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- P53 Entrez IDs -----------------
p53_entrez_ids <- as.character(c(
1026, 50484, 4193, 9766, 9518, 7832, 1643, 1647, 1263, 57103, 51065, 8795, 51499, 64393, 581, 5228, 5429, 8493, 55959,
7508, 64782, 282991, 355, 53836, 4814, 10769, 9050, 27244, 9540, 94241, 26154, 57763, 900, 26999, 55332, 26263, 23479,
23612, 29950, 9618, 10346, 8824, 134147, 55294, 22824, 4254, 6560, 467, 27113, 60492, 8444, 60401, 1969, 220965, 2232,
3976, 55191, 84284, 93129, 5564, 7803, 83667, 7779, 132671, 7039, 51768, 137695, 93134, 7633, 10973, 340485, 307,
27350, 23245, 3732, 29965, 1363, 1435, 196513, 8507, 8061, 2517, 51278, 53354, 54858, 23228, 5366, 5912, 6236, 51222,
26152, 59, 1907, 50650, 91012, 780, 9249, 11072, 144455, 64787, 116151, 27165, 2876, 57822, 55733, 57722, 121457,
375449, 85377, 4851, 5875, 127544, 29901, 84958, 8797, 8793, 441631, 220001, 54541, 5889, 5054, 25816, 25987, 5111,
98, 317, 598, 604, 10904, 1294, 80315, 53944, 1606, 2770, 3628, 3675, 3985, 4035, 4163, 84552, 29085, 55367, 5371,
5791, 54884, 5980, 8794, 1462, 50808, 220, 583, 694, 1056, 9076, 10978, 54677, 1612, 55040, 114907, 2274, 127707,
4000, 8079, 4646, 4747, 27445, 5143, 80055, 79156, 5360, 5364, 23654, 5565, 5613, 5625, 10076, 56963, 6004, 390,
255488, 6326, 6330, 23513, 7869, 283130, 204962, 83959, 6548, 6774, 9263, 10228, 22954, 10475, 85363, 494514, 10142,
79714, 1006, 8446, 9648, 79828, 5507, 55240, 63874, 25841, 9289, 84883, 154810, 51321, 421, 8553, 655, 119032, 84280,
10950, 824, 839, 57828, 857, 8812, 8837, 94027, 113189, 22837, 132864, 10898, 3300, 81704, 1847, 1849, 1947, 9538,
24139, 5168, 147965, 115548, 9873, 23768, 2632, 2817, 3280, 3265, 23308, 3490, 51477, 182, 3856, 8844, 144811, 9404,
4043, 9848, 2872, 23041, 740, 343263, 4638, 26509, 4792, 22861, 57523, 55214, 80025, 164091, 57060, 64065, 51090,
5453, 8496, 333926, 55671, 5900, 55544, 23179, 8601, 389, 6223, 55800, 6385, 4088, 6643, 122809, 257397, 285343,
7011, 54790, 374618, 55362, 51754, 7157, 9537, 22906, 7205, 80705, 219699, 55245, 83719, 7748, 25946, 118738
))
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% p53_entrez_ids, "P53 Target Genes", "Non-P53 Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% p53_entrez_ids, "P53 Target Genes", "Non-P53 Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Set Category Order (P53 first) -----------------
df_combined$Category <- factor(df_combined$Category, levels = c("P53 Target Genes", "Non-P53 Genes"))
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("P53 Target Genes", "Non-P53 Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["P53 Target Genes"], ref_counts["Non-P53 Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Run test for each concentration
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")
chi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")Warning: There was 1 warning in `summarise()`.
ℹ In argument: `p_value = { ... }`.
ℹ In group 2: `Response_Group = "DOX early + CX-DOX mid-late response"`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrectchi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
# Merge test results into proportions
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Reorder Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Star Label Position -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5,
color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c(
"P53 Target Genes" = "#E41A1C",
"Non-P53 Genes" = "#377EB8"
)) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "P53 Target Gene Proportions Across\nCormotif Clusters (0.1 and 0.5)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
📌 Mean logFC of P53 target genes across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- P53 Entrez IDs -----------------
entrez_ids <- c(
1026, 50484, 4193, 9766, 9518, 7832, 1643, 1647, 1263, 57103, 51065, 8795, 51499, 64393, 581, 5228, 5429, 8493, 55959,
7508, 64782, 282991, 355, 53836, 4814, 10769, 9050, 27244, 9540, 94241, 26154, 57763, 900, 26999, 55332, 26263, 23479,
23612, 29950, 9618, 10346, 8824, 134147, 55294, 22824, 4254, 6560, 467, 27113, 60492, 8444, 60401, 1969, 220965, 2232,
3976, 55191, 84284, 93129, 5564, 7803, 83667, 7779, 132671, 7039, 51768, 137695, 93134, 7633, 10973, 340485, 307,
27350, 23245, 3732, 29965, 1363, 1435, 196513, 8507, 8061, 2517, 51278, 53354, 54858, 23228, 5366, 5912, 6236, 51222,
26152, 59, 1907, 50650, 91012, 780, 9249, 11072, 144455, 64787, 116151, 27165, 2876, 57822, 55733, 57722, 121457,
375449, 85377, 4851, 5875, 127544, 29901, 84958, 8797, 8793, 441631, 220001, 54541, 5889, 5054, 25816, 25987, 5111,
98, 317, 598, 604, 10904, 1294, 80315, 53944, 1606, 2770, 3628, 3675, 3985, 4035, 4163, 84552, 29085, 55367, 5371,
5791, 54884, 5980, 8794, 1462, 50808, 220, 583, 694, 1056, 9076, 10978, 54677, 1612, 55040, 114907, 2274, 127707,
4000, 8079, 4646, 4747, 27445, 5143, 80055, 79156, 5360, 5364, 23654, 5565, 5613, 5625, 10076, 56963, 6004, 390,
255488, 6326, 6330, 23513, 7869, 283130, 204962, 83959, 6548, 6774, 9263, 10228, 22954, 10475, 85363, 494514, 10142,
79714, 1006, 8446, 9648, 79828, 5507, 55240, 63874, 25841, 9289, 84883, 154810, 51321, 421, 8553, 655, 119032, 84280,
10950, 824, 839, 57828, 857, 8812, 8837, 94027, 113189, 22837, 132864, 10898, 3300, 81704, 1847, 1849, 1947, 9538,
24139, 5168, 147965, 115548, 9873, 23768, 2632, 2817, 3280, 3265, 23308, 3490, 51477, 182, 3856, 8844, 144811, 9404,
4043, 9848, 2872, 23041, 740, 343263, 4638, 26509, 4792, 22861, 57523, 55214, 80025, 164091, 57060, 64065, 51090,
5453, 8496, 333926, 55671, 5900, 55544, 23179, 8601, 389, 6223, 55800, 6385, 4088, 6643, 122809, 257397, 285343,
7011, 54790, 374618, 55362, 51754, 7157, 9537, 22906, 7205, 80705, 219699, 55245, 83719, 7748, 25946, 118738
)
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% as.character(entrez_ids)) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% as.character(entrez_ids)) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM",
TRUE ~ NA_character_
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge with CorMotif P53 Genes -----------------
p53_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
p53_logfc$Response_Group <- factor(p53_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
star_df <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- p53_logfc$logFC[p53_logfc$Group == resp_group]
control_vals <- p53_logfc$logFC[p53_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
if (pval < 0.05) {
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
TRUE ~ "*"
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(Group = resp_group, y_pos = y_pos, label = label, P_Value = signif(pval, 4)))
}
}
return(NULL)
}) %>% bind_rows()
# ----------------- Prepare Annotation -----------------
label_data <- star_df %>%
separate(Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(p53_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of P53 Target Genes\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of P53 target genes across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- P53 Entrez IDs -----------------
entrez_ids <- c(
1026, 50484, 4193, 9766, 9518, 7832, 1643, 1647, 1263, 57103, 51065, 8795, 51499, 64393, 581, 5228, 5429, 8493, 55959,
7508, 64782, 282991, 355, 53836, 4814, 10769, 9050, 27244, 9540, 94241, 26154, 57763, 900, 26999, 55332, 26263, 23479,
23612, 29950, 9618, 10346, 8824, 134147, 55294, 22824, 4254, 6560, 467, 27113, 60492, 8444, 60401, 1969, 220965, 2232,
3976, 55191, 84284, 93129, 5564, 7803, 83667, 7779, 132671, 7039, 51768, 137695, 93134, 7633, 10973, 340485, 307,
27350, 23245, 3732, 29965, 1363, 1435, 196513, 8507, 8061, 2517, 51278, 53354, 54858, 23228, 5366, 5912, 6236, 51222,
26152, 59, 1907, 50650, 91012, 780, 9249, 11072, 144455, 64787, 116151, 27165, 2876, 57822, 55733, 57722, 121457,
375449, 85377, 4851, 5875, 127544, 29901, 84958, 8797, 8793, 441631, 220001, 54541, 5889, 5054, 25816, 25987, 5111,
98, 317, 598, 604, 10904, 1294, 80315, 53944, 1606, 2770, 3628, 3675, 3985, 4035, 4163, 84552, 29085, 55367, 5371,
5791, 54884, 5980, 8794, 1462, 50808, 220, 583, 694, 1056, 9076, 10978, 54677, 1612, 55040, 114907, 2274, 127707,
4000, 8079, 4646, 4747, 27445, 5143, 80055, 79156, 5360, 5364, 23654, 5565, 5613, 5625, 10076, 56963, 6004, 390,
255488, 6326, 6330, 23513, 7869, 283130, 204962, 83959, 6548, 6774, 9263, 10228, 22954, 10475, 85363, 494514, 10142,
79714, 1006, 8446, 9648, 79828, 5507, 55240, 63874, 25841, 9289, 84883, 154810, 51321, 421, 8553, 655, 119032, 84280,
10950, 824, 839, 57828, 857, 8812, 8837, 94027, 113189, 22837, 132864, 10898, 3300, 81704, 1847, 1849, 1947, 9538,
24139, 5168, 147965, 115548, 9873, 23768, 2632, 2817, 3280, 3265, 23308, 3490, 51477, 182, 3856, 8844, 144811, 9404,
4043, 9848, 2872, 23041, 740, 343263, 4638, 26509, 4792, 22861, 57523, 55214, 80025, 164091, 57060, 64065, 51090,
5453, 8496, 333926, 55671, 5900, 55544, 23179, 8601, 389, 6223, 55800, 6385, 4088, 6643, 122809, 257397, 285343,
7011, 54790, 374618, 55362, 51754, 7157, 9537, 22906, 7205, 80705, 219699, 55245, 83719, 7748, 25946, 118738
) %>% as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with P53 Targets and Annotate -----------------
p53_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID, column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(p53_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(p53_all$Concentration)) {
for (tp in levels(p53_all$Timepoint)) {
sub <- p53_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(p53_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(p53_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "P53 Target Gene logFC by\nCorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of P53 target genes across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- P53 Target Entrez IDs -----------------
p53_ids <- c(
1026, 50484, 4193, 9766, 9518, 7832, 1643, 1647, 1263, 57103, 51065, 8795, 51499, 64393, 581, 5228, 5429, 8493, 55959,
7508, 64782, 282991, 355, 53836, 4814, 10769, 9050, 27244, 9540, 94241, 26154, 57763, 900, 26999, 55332, 26263, 23479,
23612, 29950, 9618, 10346, 8824, 134147, 55294, 22824, 4254, 6560, 467, 27113, 60492, 8444, 60401, 1969, 220965, 2232,
3976, 55191, 84284, 93129, 5564, 7803, 83667, 7779, 132671, 7039, 51768, 137695, 93134, 7633, 10973, 340485, 307,
27350, 23245, 3732, 29965, 1363, 1435, 196513, 8507, 8061, 2517, 51278, 53354, 54858, 23228, 5366, 5912, 6236, 51222,
26152, 59, 1907, 50650, 91012, 780, 9249, 11072, 144455, 64787, 116151, 27165, 2876, 57822, 55733, 57722, 121457,
375449, 85377, 4851, 5875, 127544, 29901, 84958, 8797, 8793, 441631, 220001, 54541, 5889, 5054, 25816, 25987, 5111,
98, 317, 598, 604, 10904, 1294, 80315, 53944, 1606, 2770, 3628, 3675, 3985, 4035, 4163, 84552, 29085, 55367, 5371,
5791, 54884, 5980, 8794, 1462, 50808, 220, 583, 694, 1056, 9076, 10978, 54677, 1612, 55040, 114907, 2274, 127707,
4000, 8079, 4646, 4747, 27445, 5143, 80055, 79156, 5360, 5364, 23654, 5565, 5613, 5625, 10076, 56963, 6004, 390,
255488, 6326, 6330, 23513, 7869, 283130, 204962, 83959, 6548, 6774, 9263, 10228, 22954, 10475, 85363, 494514, 10142,
79714, 1006, 8446, 9648, 79828, 5507, 55240, 63874, 25841, 9289, 84883, 154810, 51321, 421, 8553, 655, 119032, 84280,
10950, 824, 839, 57828, 857, 8812, 8837, 94027, 113189, 22837, 132864, 10898, 3300, 81704, 1847, 1849, 1947, 9538,
24139, 5168, 147965, 115548, 9873, 23768, 2632, 2817, 3280, 3265, 23308, 3490, 51477, 182, 3856, 8844, 144811, 9404,
4043, 9848, 2872, 23041, 740, 343263, 4638, 26509, 4792, 22861, 57523, 55214, 80025, 164091, 57060, 64065, 51090,
5453, 8496, 333926, 55671, 5900, 55544, 23179, 8601, 389, 6223, 55800, 6385, 4088, 6643, 122809, 257397, 285343,
7011, 54790, 374618, 55362, 51754, 7157, 9537, 22906, 7205, 80705, 219699, 55245, 83719, 7748, 25946, 118738
) %>% as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% p53_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM P53) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "P53 Target Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
################### P53 Target Genes — 0.5 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- P53 Target Entrez IDs -----------------
p53_ids <- c(
1026, 50484, 4193, 9766, 9518, 7832, 1643, 1647, 1263, 57103, 51065, 8795, 51499, 64393, 581, 5228, 5429, 8493, 55959,
7508, 64782, 282991, 355, 53836, 4814, 10769, 9050, 27244, 9540, 94241, 26154, 57763, 900, 26999, 55332, 26263, 23479,
23612, 29950, 9618, 10346, 8824, 134147, 55294, 22824, 4254, 6560, 467, 27113, 60492, 8444, 60401, 1969, 220965, 2232,
3976, 55191, 84284, 93129, 5564, 7803, 83667, 7779, 132671, 7039, 51768, 137695, 93134, 7633, 10973, 340485, 307,
27350, 23245, 3732, 29965, 1363, 1435, 196513, 8507, 8061, 2517, 51278, 53354, 54858, 23228, 5366, 5912, 6236, 51222,
26152, 59, 1907, 50650, 91012, 780, 9249, 11072, 144455, 64787, 116151, 27165, 2876, 57822, 55733, 57722, 121457,
375449, 85377, 4851, 5875, 127544, 29901, 84958, 8797, 8793, 441631, 220001, 54541, 5889, 5054, 25816, 25987, 5111,
98, 317, 598, 604, 10904, 1294, 80315, 53944, 1606, 2770, 3628, 3675, 3985, 4035, 4163, 84552, 29085, 55367, 5371,
5791, 54884, 5980, 8794, 1462, 50808, 220, 583, 694, 1056, 9076, 10978, 54677, 1612, 55040, 114907, 2274, 127707,
4000, 8079, 4646, 4747, 27445, 5143, 80055, 79156, 5360, 5364, 23654, 5565, 5613, 5625, 10076, 56963, 6004, 390,
255488, 6326, 6330, 23513, 7869, 283130, 204962, 83959, 6548, 6774, 9263, 10228, 22954, 10475, 85363, 494514, 10142,
79714, 1006, 8446, 9648, 79828, 5507, 55240, 63874, 25841, 9289, 84883, 154810, 51321, 421, 8553, 655, 119032, 84280,
10950, 824, 839, 57828, 857, 8812, 8837, 94027, 113189, 22837, 132864, 10898, 3300, 81704, 1847, 1849, 1947, 9538,
24139, 5168, 147965, 115548, 9873, 23768, 2632, 2817, 3280, 3265, 23308, 3490, 51477, 182, 3856, 8844, 144811, 9404,
4043, 9848, 2872, 23041, 740, 343263, 4638, 26509, 4792, 22861, 57523, 55214, 80025, 164091, 57060, 64065, 51090,
5453, 8496, 333926, 55671, 5900, 55544, 23179, 8601, 389, 6223, 55800, 6385, 4088, 6643, 122809, 257397, 285343,
7011, 54790, 374618, 55362, 51754, 7157, 9537, 22906, 7205, 80705, 219699, 55245, 83719, 7748, 25946, 118738
) %>% as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% p53_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM P53) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "P53 Target Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 TOP2B target genes proportions across corrmotifs
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
library(readr)
# ----------------- Load TOP2B Entrez IDs -----------------
top2b_entrez_ids <- read_csv("data/TOP2B_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% top2b_entrez_ids, "TOP2B Target Genes", "Non-TOP2B Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% top2b_entrez_ids, "TOP2B Target Genes", "Non-TOP2B Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Set Category Order (TOP2B first) -----------------
df_combined$Category <- factor(df_combined$Category, levels = c("TOP2B Target Genes", "Non-TOP2B Genes"))
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("TOP2B Target Genes", "Non-TOP2B Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["TOP2B Target Genes"], ref_counts["Non-TOP2B Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Run test for each concentration
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")
chi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")
chi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
# Merge test results into proportions
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Reorder Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Star Label Position -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5,
color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c(
"TOP2B Target Genes" = "#E41A1C",
"Non-TOP2B Genes" = "#377EB8"
)) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "TOP2B Target Gene Proportions Across\nCormotif Clusters (0.1 and 0.5)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2B target genes across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- TOP2B Entrez IDs -----------------
top2b_entrez_ids <- read_csv("data/TOP2B_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% top2b_entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% top2b_entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM",
TRUE ~ NA_character_
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge with CorMotif TOP2B Genes -----------------
top2b_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
top2b_logfc$Response_Group <- factor(top2b_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
star_df <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- top2b_logfc$logFC[top2b_logfc$Group == resp_group]
control_vals <- top2b_logfc$logFC[top2b_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
if (pval < 0.05) {
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
TRUE ~ "*"
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(Group = resp_group, y_pos = y_pos, label = label, P_Value = signif(pval, 4)))
}
}
return(NULL)
}) %>% bind_rows()
# ----------------- Prepare Annotation -----------------
label_data <- star_df %>%
separate(Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(top2b_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of TOP2B Target Genes\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2B target genes across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- TOP2B Entrez IDs -----------------
top2b_entrez_ids <- read_csv("data/TOP2B_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% top2b_entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% top2b_entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with TOP2B Targets and Annotate -----------------
top2b_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% top2b_entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID, column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(top2b_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(top2b_all$Concentration)) {
for (tp in levels(top2b_all$Timepoint)) {
sub <- top2b_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(top2b_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(top2b_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "TOP2B Target Gene logFC by\nCorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2B target genes across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
################### TOP2B Target Genes — 0.1 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2B Target Entrez IDs -----------------
top2b_entrez_ids <- read_csv("data/TOP2B_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% top2b_entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM TOP2B) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "TOP2B Target Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
################### TOP2B Target Genes — 0.5 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2B Target Entrez IDs -----------------
top2b_entrez_ids <- read_csv("data/TOP2B_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% top2b_entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM TOP2B) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "TOP2B Target Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 TOP2A target genes (ChIP_Atlas) proportion across corrmotifs
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
library(readr)
# ----------------- Load TOP2A Entrez IDs -----------------
top2a_entrez_ids <- read_csv("data/TOP2A_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% top2a_entrez_ids, "TOP2A Target Genes", "Non-TOP2A Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% top2a_entrez_ids, "TOP2A Target Genes", "Non-TOP2A Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Set Category Order -----------------
df_combined$Category <- factor(df_combined$Category, levels = c("TOP2A Target Genes", "Non-TOP2A Genes"))
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("TOP2A Target Genes", "Non-TOP2A Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["TOP2A Target Genes"], ref_counts["Non-TOP2A Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Run test for each concentration
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")
chi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")Warning: There was 1 warning in `summarise()`.
ℹ In argument: `p_value = { ... }`.
ℹ In group 4: `Response_Group = "DOX-specific response"`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrectchi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
# Merge test results into proportions
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Reorder Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Star Label Position -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5,
color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c(
"TOP2A Target Genes" = "#E41A1C",
"Non-TOP2A Genes" = "#377EB8"
)) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "TOP2A Target Gene Proportions Across\nCormotif Clusters (0.1 and 0.5)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2A target genes (ChIP_Atlas) across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2A Entrez IDs -----------------
top2a_entrez_ids <- read_csv("data/TOP2A_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% top2a_entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% top2a_entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM",
TRUE ~ NA_character_
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge with CorMotif TOP2A Genes -----------------
top2a_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define Response Group Order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
top2a_logfc$Response_Group <- factor(top2a_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test with Output -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
wilcoxon_results <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- top2a_logfc$logFC[top2a_logfc$Group == resp_group]
control_vals <- top2a_logfc$logFC[top2a_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
stat <- test_result$statistic
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
TRUE ~ ""
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(
Response_Group = resp_group,
Control_Group = control_group,
W_statistic = as.numeric(stat),
P_value = signif(pval, 4),
Significance = label,
y_pos = y_pos
))
} else {
return(data.frame(
Response_Group = resp_group,
Control_Group = control_group,
W_statistic = NA,
P_value = NA,
Significance = "",
y_pos = NA
))
}
}) %>% bind_rows()
# ----------------- Prepare Annotation -----------------
label_data <- wilcoxon_results %>%
separate(Response_Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(top2a_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of TOP2A Target Genes\nAcross CorMotif Response Groups (ChIP atlas)",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2A target genes (ChIP_Atlas) across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2A Entrez IDs -----------------
top2a_entrez_ids <- read_csv("data/TOP2A_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% top2a_entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% top2a_entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with TOP2A Targets and Annotate -----------------
top2a_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% top2a_entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID, column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(top2a_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(top2a_all$Concentration)) {
for (tp in levels(top2a_all$Timepoint)) {
sub <- top2a_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(top2a_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(top2a_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "TOP2A Target Gene logFC by\nCorMotif Group, Timepoint, and Concentration (ChIP Atlas)",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2A target genes (ChIP_Atlas) across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2A ChIP-Atlas Entrez IDs -----------------
top2a_entrez_ids <- read_csv("data/TOP2A_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% top2a_entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM TOP2A) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "TOP2A Target Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
################### TOP2A Target Genes (ChIP-Atlas) — 0.5 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2A Entrez IDs -----------------
top2a_entrez_ids <- read_csv("data/TOP2A_target_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% top2a_entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM TOP2A ChIP-Atlas) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "TOP2A (ChIP-Atlas) Target Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Proportion of TOP2A target genes (RPE-1) across corrmotifs
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
library(readr)
# ----------------- Load TOP2A (RPE-1) Entrez IDs -----------------
top2a_lit_ids <- read_csv("data/TOP2A_target_lit_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% top2a_lit_ids, "TOP2A (RPE-1) Targets", "Non-TOP2A Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% top2a_lit_ids, "TOP2A (RPE-1) Targets", "Non-TOP2A Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Set Category Order -----------------
df_combined$Category <- factor(df_combined$Category, levels = c("TOP2A (RPE-1) Targets", "Non-TOP2A Genes"))
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("TOP2A (RPE-1) Targets", "Non-TOP2A Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["TOP2A (RPE-1) Targets"], ref_counts["Non-TOP2A Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Run test for each concentration
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")
chi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")
chi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
# Merge test results into proportions
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Reorder Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Star Label Position -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5,
color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c(
"TOP2A (RPE-1) Targets" = "#E41A1C",
"Non-TOP2A Genes" = "#377EB8"
)) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "TOP2A (RPE-1) Target Gene Proportions Across\nCormotif Clusters (0.1 and 0.5)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2A target genes (RPE-1) across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2A (RPE-1) Entrez IDs -----------------
top2a_lit_ids <- read_csv("data/TOP2A_target_lit_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% top2a_lit_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% top2a_lit_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM",
TRUE ~ NA_character_
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge with CorMotif Genes -----------------
top2a_lit_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define Response Group Order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
top2a_lit_logfc$Response_Group <- factor(top2a_lit_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test with Output -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
wilcoxon_results <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- top2a_lit_logfc$logFC[top2a_lit_logfc$Group == resp_group]
control_vals <- top2a_lit_logfc$logFC[top2a_lit_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
stat <- test_result$statistic
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
TRUE ~ ""
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(
Response_Group = resp_group,
Control_Group = control_group,
W_statistic = as.numeric(stat),
P_value = signif(pval, 4),
Significance = label,
y_pos = y_pos
))
} else {
return(data.frame(
Response_Group = resp_group,
Control_Group = control_group,
W_statistic = NA,
P_value = NA,
Significance = "",
y_pos = NA
))
}
}) %>% bind_rows()
# ----------------- Prepare Annotation -----------------
label_data <- wilcoxon_results %>%
separate(Response_Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(top2a_lit_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of Literature-Based TOP2A Targets (RPE-1)\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2A target genes (RPE-1) across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2A (RPE-1) Entrez IDs -----------------
top2a_lit_ids <- read_csv("data/TOP2A_target_lit_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% top2a_lit_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% top2a_lit_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with TOP2A (Lit) Targets and Annotate -----------------
top2a_lit_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% top2a_lit_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID, column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(top2a_lit_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(top2a_lit_all$Concentration)) {
for (tp in levels(top2a_lit_all$Timepoint)) {
sub <- top2a_lit_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(top2a_lit_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(top2a_lit_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "TOP2A Target Gene (RPE-1 cell) logFC by\nCorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of TOP2A target genes (RPE-1) across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
################### TOP2A Target Genes (RPE-1 Literature) — 0.1 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2A (RPE-1) Entrez IDs -----------------
top2a_lit_ids <- read_csv("data/TOP2A_target_lit_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% top2a_lit_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM TOP2A RPE-1) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "TOP2A (RPE-1) Target Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
################### TOP2A Target Genes (RPE-1 Literature) — 0.5 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load TOP2A (RPE-1) Entrez IDs -----------------
top2a_lit_ids <- read_csv("data/TOP2A_target_lit_mapped.csv", show_col_types = FALSE) %>%
pull(Entrez_ID) %>%
unique() %>%
as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% top2a_lit_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM TOP2A RPE-1) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "TOP2A (RPE-1) Target Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Proportion of Anthracycline Cardiotoxicity genes across corrmotifs
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
# ----------------- AC Cardiotoxicity Entrez IDs -----------------
ac_cardio_ids <- c(
6272, 8029, 11128, 79899, 54477, 121665, 5095, 22863, 57161, 4692,
8214, 23151, 56606, 108, 22999, 56895, 9603, 3181, 4023, 10499,
92949, 4363, 10057, 5243, 5244, 5880, 1535, 2950, 847, 5447,
3038, 3077, 4846, 3958, 23327, 29899, 23155, 80856, 55020, 78996,
23262, 150383, 9620, 79730, 344595, 5066, 6251, 3482, 9588, 339416,
7292, 55157, 87769, 23409, 720, 3107, 54535, 1590, 80059, 7991,
57110, 8803, 323, 54826, 5916, 23371, 283337, 64078, 80010, 1933,
10818, 51020
) %>% as.character()
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% ac_cardio_ids, "AC-Toxicity Genes", "Non-AC Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% ac_cardio_ids, "AC-Toxicity Genes", "Non-AC Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Set Category Order -----------------
df_combined$Category <- factor(df_combined$Category, levels = c("AC-Toxicity Genes", "Non-AC Genes"))
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("AC-Toxicity Genes", "Non-AC Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["AC-Toxicity Genes"], ref_counts["Non-AC Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Run test for each concentration
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")
chi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")
chi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
# Merge test results into proportions
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Reorder Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Star Label Position -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5,
color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c(
"AC-Toxicity Genes" = "#E41A1C",
"Non-AC Genes" = "#377EB8"
)) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "AC Cardiotoxicity Gene Proportions Across\nCormotif Clusters (0.1 and 0.5)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of Anthracycline Cardiotoxicity genes across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- AC Cardiotoxicity Entrez IDs -----------------
entrez_ids <- c(
6272, 8029, 11128, 79899, 54477, 121665, 5095, 22863, 57161, 4692,
8214, 23151, 56606, 108, 22999, 56895, 9603, 3181, 4023, 10499,
92949, 4363, 10057, 5243, 5244, 5880, 1535, 2950, 847, 5447,
3038, 3077, 4846, 3958, 23327, 29899, 23155, 80856, 55020, 78996,
23262, 150383, 9620, 79730, 344595, 5066, 6251, 3482, 9588, 339416,
7292, 55157, 87769, 23409, 720, 3107, 54535, 1590, 80059, 7991,
57110, 8803, 323, 54826, 5916, 23371, 283337, 64078, 80010, 1933,
10818, 51020
) %>% as.character()
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM",
TRUE ~ NA_character_
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge with CorMotif Genes -----------------
ac_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define Group Order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
ac_logfc$Response_Group <- factor(ac_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
wilcoxon_results <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- ac_logfc$logFC[ac_logfc$Group == resp_group]
control_vals <- ac_logfc$logFC[ac_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
stat <- test_result$statistic
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
TRUE ~ ""
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(
Group = resp_group,
y_pos = y_pos,
label = label,
P_Value = signif(pval, 4)
))
} else {
return(NULL)
}
}) %>% bind_rows()
# ----------------- Prepare Annotation -----------------
label_data <- wilcoxon_results %>%
separate(Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(ac_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of AC Cardiotoxicity Genes\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of Anthracycline Cardiotoxicity genes across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- AC Cardiotoxicity Entrez IDs -----------------
entrez_ids <- c(
6272, 8029, 11128, 79899, 54477, 121665, 5095, 22863, 57161, 4692,
8214, 23151, 56606, 108, 22999, 56895, 9603, 3181, 4023, 10499,
92949, 4363, 10057, 5243, 5244, 5880, 1535, 2950, 847, 5447,
3038, 3077, 4846, 3958, 23327, 29899, 23155, 80856, 55020, 78996,
23262, 150383, 9620, 79730, 344595, 5066, 6251, 3482, 9588, 339416,
7292, 55157, 87769, 23409, 720, 3107, 54535, 1590, 80059, 7991,
57110, 8803, 323, 54826, 5916, 23371, 283337, 64078, 80010, 1933,
10818, 51020
) %>% as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with AC Cardiotoxicity Targets -----------------
ac_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID, column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(ac_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(ac_all$Concentration)) {
for (tp in levels(ac_all$Timepoint)) {
sub <- ac_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(ac_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(ac_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "AC Cardiotoxicity Gene logFC by\nCorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of Anthracycline Cardiotoxicity genes across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
################### AC Cardiotoxicity Genes — 0.1 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- AC Cardiotoxicity Entrez IDs -----------------
ac_ids <- c(
6272, 8029, 11128, 79899, 54477, 121665, 5095, 22863, 57161, 4692,
8214, 23151, 56606, 108, 22999, 56895, 9603, 3181, 4023, 10499,
92949, 4363, 10057, 5243, 5244, 5880, 1535, 2950, 847, 5447,
3038, 3077, 4846, 3958, 23327, 29899, 23155, 80856, 55020, 78996,
23262, 150383, 9620, 79730, 344595, 5066, 6251, 3482, 9588, 339416,
7292, 55157, 87769, 23409, 720, 3107, 54535, 1590, 80059, 7991,
57110, 8803, 323, 54826, 5916, 23371, 283337, 64078, 80010, 1933,
10818, 51020
) %>% as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% ac_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM AC Cardiotoxicity) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "AC Cardiotoxicity Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
################### AC Genes — 0.5 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- AC Cardiotoxicity Entrez IDs -----------------
ac_ids <- c(
6272, 8029, 11128, 79899, 54477, 121665, 5095, 22863, 57161, 4692,
8214, 23151, 56606, 108, 22999, 56895, 9603, 3181, 4023, 10499,
92949, 4363, 10057, 5243, 5244, 5880, 1535, 2950, 847, 5447,
3038, 3077, 4846, 3958, 23327, 29899, 23155, 80856, 55020, 78996,
23262, 150383, 9620, 79730, 344595, 5066, 6251, 3482, 9588, 339416,
7292, 55157, 87769, 23409, 720, 3107, 54535, 1590, 80059, 7991,
57110, 8803, 323, 54826, 5916, 23371, 283337, 64078, 80010, 1933,
10818, 51020
) %>% as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% ac_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM AC Genes) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "AC Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Proportion of DOX Cardiotoxicity genes across corrmotifs
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
# ----------------- DOX Cardiotoxicity Entrez IDs -----------------
dox_cardio_ids <- c(
847, 873, 2064, 2878, 2944, 3038, 4846, 51196, 5880, 6687,
7799, 4292, 5916, 3077, 51310, 9154, 64078, 5244, 10057, 10060,
89845, 56853, 4625, 1573, 79890
) %>% as.character()
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% dox_cardio_ids, "DOX-Toxicity Genes", "Non-DOX Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% dox_cardio_ids, "DOX-Toxicity Genes", "Non-DOX Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Set Category Order -----------------
df_combined$Category <- factor(df_combined$Category, levels = c("DOX-Toxicity Genes", "Non-DOX Genes"))
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("DOX-Toxicity Genes", "Non-DOX Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["DOX-Toxicity Genes"], ref_counts["Non-DOX Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Run test for each concentration
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")Warning: There was 1 warning in `summarise()`.
ℹ In argument: `p_value = { ... }`.
ℹ In group 2: `Response_Group = "DOX only mid-late response"`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrectchi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")
chi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
# Merge test results into proportions
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Reorder Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Star Label Position -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5,
color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c(
"DOX-Toxicity Genes" = "#E41A1C",
"Non-DOX Genes" = "#377EB8"
)) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "DOX Cardiotoxicity Gene Proportions Across\nCormotif Clusters (0.1 and 0.5)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of DOX Cardiotoxicity genes across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DOX Cardiotoxicity Entrez IDs -----------------
entrez_ids <- c(
847, 873, 2064, 2878, 2944, 3038, 4846, 51196, 5880, 6687,
7799, 4292, 5916, 3077, 51310, 9154, 64078, 5244, 10057, 10060,
89845, 56853, 4625, 1573, 79890
) %>% as.character()
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM",
TRUE ~ NA_character_
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge with CorMotif Genes -----------------
dox_cardio_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define Response Group Order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
dox_cardio_logfc$Response_Group <- factor(dox_cardio_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
wilcoxon_results <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- dox_cardio_logfc$logFC[dox_cardio_logfc$Group == resp_group]
control_vals <- dox_cardio_logfc$logFC[dox_cardio_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
stat <- test_result$statistic
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
TRUE ~ ""
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(
Group = resp_group,
y_pos = y_pos,
label = label,
P_Value = signif(pval, 4)
))
} else {
return(NULL)
}
}) %>% bind_rows()
# ----------------- Prepare Annotation -----------------
label_data <- wilcoxon_results %>%
separate(Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(dox_cardio_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of DOX Cardiotoxicity Genes\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of DOX Cardiotoxicity genes across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DOX Cardiotoxicity Entrez IDs -----------------
entrez_ids <- c(
847, 873, 2064, 2878, 2944, 3038, 4846, 51196, 5880, 6687,
7799, 4292, 5916, 3077, 51310, 9154, 64078, 5244, 10057, 10060,
89845, 56853, 4625, 1573, 79890
) %>% as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with DOX Cardiotoxicity Targets -----------------
dox_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID, column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(dox_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(dox_all$Concentration)) {
for (tp in levels(dox_all$Timepoint)) {
sub <- dox_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(dox_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(dox_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "DOX Cardiotoxicity Gene logFC by\nCorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of DOX Cardiotoxicity genes across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
################### DOX Cardiotoxicity Genes — 0.1 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DOX Cardiotoxicity Entrez IDs -----------------
entrez_ids <- c(
847, 873, 2064, 2878, 2944, 3038, 4846, 51196, 5880, 6687,
7799, 4292, 5916, 3077, 51310, 9154, 64078, 5244, 10057, 10060,
89845, 56853, 4625, 1573, 79890
) %>% as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM DOX Cardiotoxicity Genes) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "DOX Cardiotoxicity Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
################### DOX Cardiotoxicity Genes — 0.5 µM ###################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- DOX Cardiotoxicity Entrez IDs -----------------
entrez_ids <- c(
847, 873, 2064, 2878, 2944, 3038, 4846, 51196, 5880, 6687,
7799, 4292, 5916, 3077, 51310, 9154, 64078, 5244, 10057, 10060,
89845, 56853, 4625, 1573, 79890
) %>% as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM DOX Cardiotoxicity Genes) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(width = 0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "DOX Cardiotoxicity Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Proportion of Heart Specific Genes (Combined)
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load Heart-Specific Genes -----------------
heart_genes <- read.csv("data/Human_Heart_Genes.csv", stringsAsFactors = FALSE)
heart_genes$Entrez_ID <- mapIds(
org.Hs.eg.db,
keys = heart_genes$Gene,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
heart_entrez_ids <- na.omit(heart_genes$Entrez_ID)
# ----------------- Load CorrMotif Groups -----------------
# 0.1
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% heart_entrez_ids, "Heart-specific Genes", "Non-Heart-specific Genes"),
Concentration = "0.1"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% heart_entrez_ids, "Heart-specific Genes", "Non-Heart-specific Genes"),
Concentration = "0.5"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Fisher's Exact Test -----------------
run_fisher_test <- function(df, ref_group) {
# Get reference counts
ref_counts <- df %>%
dplyr::ungroup() %>%
dplyr::filter(Response_Group == ref_group) %>%
dplyr::select(Category, Count) %>%
{setNames(.$Count, .$Category)}
# Run Fisher test for each group vs. reference
df %>%
dplyr::ungroup() %>%
dplyr::filter(Response_Group != ref_group) %>%
dplyr::group_by(Response_Group) %>%
dplyr::summarise(
p_value = {
group_counts <- Count[Category %in% c("Heart-specific Genes", "Non-Heart-specific Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["Heart-specific Genes"], ref_counts["Non-Heart-specific Genes"]
), nrow = 2)
fisher.test(contingency_table)$p.value
},
.groups = "drop"
) %>%
dplyr::mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# Run Fisher test
fisher_0.1 <- run_fisher_test(proportion_data %>% filter(Concentration == "0.1"), "Non response")
fisher_0.5 <- run_fisher_test(proportion_data %>% filter(Concentration == "0.5"), "Non response")
fisher_all <- bind_rows(
fisher_0.1 %>% mutate(Concentration = "0.1"),
fisher_0.5 %>% mutate(Concentration = "0.5")
)
# ----------------- Merge Fisher Results -----------------
proportion_data <- proportion_data %>%
left_join(fisher_all, by = c("Concentration", "Response_Group"))
# ----------------- Reorder Factor Levels -----------------
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1", "0.5"))
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
# ----------------- Significance Star Labels -----------------
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance)) %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(
data = label_data,
aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE,
size = 5,
color = "black"
) +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c(
"Heart-specific Genes" = "#4daf4a",
"Non-Heart-specific Genes" = "#377eb8"
)) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "Heart-Specific Gene Proportions Across\nCormotif Clusters (0.1 and 0.5 µM)",
x = "Response Groups",
y = "Percentage of Genes",
fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
📌 Mean logFC of Heart specific genes across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load Heart-Specific Genes -----------------
heart_genes <- read.csv("data/Human_Heart_Genes.csv", stringsAsFactors = FALSE)
heart_genes$Entrez_ID <- mapIds(
org.Hs.eg.db,
keys = heart_genes$Gene,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
entrez_ids <- na.omit(heart_genes$Entrez_ID) %>% as.character()
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>% mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge with Group Annotations -----------------
heart_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define Response Group Order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
heart_logfc$Response_Group <- factor(heart_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
wilcoxon_results <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- heart_logfc$logFC[heart_logfc$Group == resp_group]
control_vals <- heart_logfc$logFC[heart_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
TRUE ~ ""
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(
Group = resp_group,
y_pos = y_pos,
label = label,
P_Value = signif(pval, 4)
))
}
return(NULL)
}) %>% bind_rows()
# ----------------- Annotation Data -----------------
label_data <- wilcoxon_results %>%
separate(Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(heart_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of Heart-Specific Genes\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
📌 Mean logFC of Heart specific genes across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load Heart-Specific Genes -----------------
heart_genes <- read.csv("data/Human_Heart_Genes.csv", stringsAsFactors = FALSE)
heart_genes$Entrez_ID <- mapIds(
org.Hs.eg.db,
keys = heart_genes$Gene,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
entrez_ids <- na.omit(heart_genes$Entrez_ID) %>% as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with Heart-Specific Targets -----------------
heart_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID, column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(heart_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(heart_all$Concentration)) {
for (tp in levels(heart_all$Timepoint)) {
sub <- heart_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(heart_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(heart_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Heart-Specific Gene logFC by\nCorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
📌 Mean logFC of Heart specific genes across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
############ HEART-SPECIFIC GENES — 0.1 µM #######################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load Heart-Specific Genes -----------------
heart_genes <- read.csv("data/Human_Heart_Genes.csv", stringsAsFactors = FALSE)
heart_genes$Entrez_ID <- mapIds(
org.Hs.eg.db,
keys = heart_genes$Gene,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
entrez_ids <- na.omit(heart_genes$Entrez_ID) %>% as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM Heart-Specific Genes) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "Heart-Specific Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
############ HEART-SPECIFIC GENES — 0.5 µM #######################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load Heart-Specific Genes -----------------
heart_genes <- read.csv("data/Human_Heart_Genes.csv", stringsAsFactors = FALSE)
heart_genes$Entrez_ID <- mapIds(
org.Hs.eg.db,
keys = heart_genes$Gene,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
entrez_ids <- na.omit(heart_genes$Entrez_ID) %>% as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM Heart-Specific Genes) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "Heart-Specific Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
📌 Proportion of Atrial Fibrillation GWAS genes across corrmotifs
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
# ----------------- AF Entrez IDs -----------------
af_entrez_ids <- c(
3425, 53834, 9464, 79658, 55876, 3781, 9472, 11017, 64395, 23353,
57794, 79591, 2702, 51306, 857, 10418, 9644, 115509, 89797, 10728,
282996, 8497, 5695, 9026, 57666, 143384, 5883, 90102, 84909, 221037,
29119, 79933, 391, 81575, 8738, 5396, 26010, 744, 56981, 80724,
5781, 6910, 4598, 79068, 2250, 56978, 3752, 5208, 9736, 26778,
80315, 64374, 79159, 11078, 5308, 3782, 10654, 79776, 6310, 8082,
2995, 23224, 79741, 5708, 26136, 9967, 84033, 9148, 29982, 81488,
1788, 9208, 2908, 1021, 5339, 5727, 23118, 10090, 57585, 221154,
1073, 29998, 817, 858, 3486, 5747, 2104, 55105, 4194, 3570,
1607, 6586, 5570, 2070, 27145, 4646, 3159, 9748, 79991, 221035,
5033, 80212, 196385, 79600, 11046, 54014, 151636, 64778, 92597, 3983,
7090, 9267, 7273, 26018, 9948, 2131, 55870, 5523, 5318, 6239,
3480, 55777, 2066, 11165, 2028, 57619, 2690, 23414, 55818, 84700,
28965, 80204, 463, 5108, 222553, 387119, 28981, 11113, 10301, 995,
23030, 2697, 996, 339500, 54805, 9960, 91404, 145781, 100820829, 84542,
2176, 51684, 9513, 7473, 4666, 23150, 5915, 5062, 4016, 23039,
159686, 1839, 5201, 93166, 64753, 29959, 5496, 23245, 5069, 56916,
92344, 23092, 3992, 9415, 10554, 7456, 9570, 57178, 23143, 161176,
5424, 2034, 10277, 11278, 79803, 6653, 4756, 132660, 5430, 9031,
57158, 285761, 8110, 387700, 1829, 4126, 7323, 51308, 7332, 6598,
3757, 6187, 6660, 10529, 6920, 115286, 8451, 8943, 4137, 7514,
4801, 9709, 23177, 8671, 29915, 26207, 3680, 490, 493856, 58489,
54897, 4625, 22955, 84952, 10221, 2263, 84641, 4892, 1026, 84650,
8382, 221656, 2969, 144453, 117177, 2626, 8476, 161882, 51807, 4624,
9612, 55795, 51043, 144348, 51232, 8729, 3899, 11155, 23316, 79006,
146330, 6403, 6331, 4300, 427, 845, 3313, 113622, 5789, 376132,
29841, 8462, 203859, 401397, 5506, 55521, 5819, 4059, 6934, 57727,
23066, 79568, 83478, 10087, 9586, 222194, 10466, 10499, 58499, 79720,
4772, 10794, 125919, 602, 27332, 59345, 340359, 3705, 64710, 57801,
10818, 143684, 149281, 55013, 23095, 93649, 84034, 23347, 440926, 11124,
23293, 51426, 832, 7068, 57646, 152002, 7531, 1398, 100101267, 221937,
26873, 3709, 10576, 27040, 201176, 284403, 307, 6525, 5387, 1808,
114907, 1952, 4091, 5096, 23451, 51207, 142891, 4084, 3797, 1185,
5334, 217, 2042, 7781, 253461, 152404, 2992, 153478, 6695, 26249,
662, 23036, 22852, 23411, 23387, 6786, 7690, 10021, 8925, 5595,
63892, 23466, 11149, 139411, 755, 55347, 22820, 55111, 84444, 6258,
57623, 1387, 3762, 5861, 3339, 114822, 129787, 196528, 54437, 10395,
4023, 5095, 894, 3156, 1877, 283871, 2673, 23157, 10512, 2258,
79750, 84665, 26091, 5978, 8751, 79695, 2768, 155382, 84163, 7709,
4092, 1837, 2064, 4629, 55122, 8882, 150962, 23013, 8742, 489,
201134, 55114, 84264, 64428, 4089, 861, 7422
) %>% as.character()
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% af_entrez_ids, "AF Genes", "Non-AF Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% af_entrez_ids, "AF Genes", "Non-AF Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
df_combined$Category <- factor(df_combined$Category, levels = c("AF Genes", "Non-AF Genes"))
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("AF Genes", "Non-AF Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["AF Genes"], ref_counts["Non-AF Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# ----------------- Run test -----------------
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")
chi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")Warning: There was 1 warning in `summarise()`.
ℹ In argument: `p_value = { ... }`.
ℹ In group 4: `Response_Group = "DOX-specific response"`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrectchi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Factor Order and Star Label -----------------
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c("AF Genes" = "#E41A1C", "Non-AF Genes" = "#377EB8")) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "AF-Associated Gene Proportions Across\nCormotif Clusters (0.1 and 0.5 µM)",
x = "Response Groups", y = "Percentage of Genes", fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of Atrial Fibrillation GWAS genes across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- AF Entrez IDs -----------------
entrez_ids <- c(
3425, 53834, 9464, 79658, 55876, 3781, 9472, 11017, 64395, 23353,
57794, 79591, 2702, 51306, 857, 10418, 9644, 115509, 89797, 10728,
282996, 8497, 5695, 9026, 57666, 143384, 5883, 90102, 84909, 221037,
29119, 79933, 391, 81575, 8738, 5396, 26010, 744, 56981, 80724,
5781, 6910, 4598, 79068, 2250, 56978, 3752, 5208, 9736, 26778,
80315, 64374, 79159, 11078, 5308, 3782, 10654, 79776, 6310, 8082,
2995, 23224, 79741, 5708, 26136, 9967, 84033, 9148, 29982, 81488,
1788, 9208, 2908, 1021, 5339, 5727, 23118, 10090, 57585, 221154,
1073, 29998, 817, 858, 3486, 5747, 2104, 55105, 4194, 3570,
1607, 6586, 5570, 2070, 27145, 4646, 3159, 9748, 79991, 221035,
5033, 80212, 196385, 79600, 11046, 54014, 151636, 64778, 92597, 3983,
7090, 9267, 7273, 26018, 9948, 2131, 55870, 5523, 5318, 6239,
3480, 55777, 2066, 11165, 2028, 57619, 2690, 23414, 55818, 84700,
28965, 80204, 463, 5108, 222553, 387119, 28981, 11113, 10301, 995,
23030, 2697, 996, 339500, 54805, 9960, 91404, 145781, 100820829, 84542,
2176, 51684, 9513, 7473, 4666, 23150, 5915, 5062, 4016, 23039,
159686, 1839, 5201, 93166, 64753, 29959, 5496, 23245, 5069, 56916,
92344, 23092, 3992, 9415, 10554, 7456, 9570, 57178, 23143, 161176,
5424, 2034, 10277, 11278, 79803, 6653, 4756, 132660, 5430, 9031,
57158, 285761, 8110, 387700, 1829, 4126, 7323, 51308, 7332, 6598,
3757, 6187, 6660, 10529, 6920, 115286, 8451, 8943, 4137, 7514,
4801, 9709, 23177, 8671, 29915, 26207, 3680, 490, 493856, 58489,
54897, 4625, 22955, 84952, 10221, 2263, 84641, 4892, 1026, 84650,
8382, 221656, 2969, 144453, 117177, 2626, 8476, 161882, 51807, 4624,
9612, 55795, 51043, 144348, 51232, 8729, 3899, 11155, 23316, 79006,
146330, 6403, 6331, 4300, 427, 845, 3313, 113622, 5789, 376132,
29841, 8462, 203859, 401397, 5506, 55521, 5819, 4059, 6934, 57727,
23066, 79568, 83478, 10087, 9586, 222194, 10466, 10499, 58499, 79720,
4772, 10794, 125919, 602, 27332, 59345, 340359, 3705, 64710, 57801,
10818, 143684, 149281, 55013, 23095, 93649, 84034, 23347, 440926, 11124,
23293, 51426, 832, 7068, 57646, 152002, 7531, 1398, 100101267, 221937,
26873, 3709, 10576, 27040, 201176, 284403, 307, 6525, 5387, 1808,
114907, 1952, 4091, 5096, 23451, 51207, 142891, 4084, 3797, 1185,
5334, 217, 2042, 7781, 253461, 152404, 2992, 153478, 6695, 26249,
662, 23036, 22852, 23411, 23387, 6786, 7690, 10021, 8925, 5595,
63892, 23466, 11149, 139411, 755, 55347, 22820, 55111, 84444, 6258,
57623, 1387, 3762, 5861, 3339, 114822, 129787, 196528, 54437, 10395,
4023, 5095, 894, 3156, 1877, 283871, 2673, 23157, 10512, 2258,
79750, 84665, 26091, 5978, 8751, 79695, 2768, 155382, 84163, 7709,
4092, 1837, 2064, 4629, 55122, 8882, 150962, 23013, 8742, 489,
201134, 55114, 84264, 64428, 4089, 861, 7422
) %>% as.character()
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>% mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge and Annotate -----------------
af_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Response Group Order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
af_logfc$Response_Group <- factor(af_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
wilcoxon_results <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- af_logfc$logFC[af_logfc$Group == resp_group]
control_vals <- af_logfc$logFC[af_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
TRUE ~ ""
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(Group = resp_group, y_pos = y_pos, label = label, P_Value = signif(pval, 4)))
}
return(NULL)
}) %>% bind_rows()
label_data <- wilcoxon_results %>%
separate(Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(af_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of Atrial Fibrillation Genes\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of Atrial Fibrillation GWAS genes across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Load Heart-Specific Genes -----------------
heart_genes <- read.csv("data/Human_Heart_Genes.csv", stringsAsFactors = FALSE)
heart_genes$Entrez_ID <- mapIds(
org.Hs.eg.db,
keys = heart_genes$Gene,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
entrez_ids <- na.omit(heart_genes$Entrez_ID) %>% as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Annotate DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge with Heart-Specific Targets -----------------
heart_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID, column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Test -----------------
comparison_groups <- levels(heart_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(heart_all$Concentration)) {
for (tp in levels(heart_all$Timepoint)) {
sub <- heart_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(heart_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(heart_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Heart-Specific Gene logFC by\nCorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
📌 Mean logFC of Atrial Fibrillation GWAS genes across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
############ AF GENES — 0.1 µM #######################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- AF Entrez IDs -----------------
entrez_ids <- c(
3425, 53834, 9464, 79658, 55876, 3781, 9472, 11017, 64395, 23353,
57794, 79591, 2702, 51306, 857, 10418, 9644, 115509, 89797, 10728,
282996, 8497, 5695, 9026, 57666, 143384, 5883, 90102, 84909, 221037,
29119, 79933, 391, 81575, 8738, 5396, 26010, 744, 56981, 80724,
5781, 6910, 4598, 79068, 2250, 56978, 3752, 5208, 9736, 26778,
80315, 64374, 79159, 11078, 5308, 3782, 10654, 79776, 6310, 8082,
2995, 23224, 79741, 5708, 26136, 9967, 84033, 9148, 29982, 81488,
1788, 9208, 2908, 1021, 5339, 5727, 23118, 10090, 57585, 221154,
1073, 29998, 817, 858, 3486, 5747, 2104, 55105, 4194, 3570,
1607, 6586, 5570, 2070, 27145, 4646, 3159, 9748, 79991, 221035,
5033, 80212, 196385, 79600, 11046, 54014, 151636, 64778, 92597, 3983,
7090, 9267, 7273, 26018, 9948, 2131, 55870, 5523, 5318, 6239,
3480, 55777, 2066, 11165, 2028, 57619, 2690, 23414, 55818, 84700,
28965, 80204, 463, 5108, 222553, 387119, 28981, 11113, 10301, 995,
23030, 2697, 996, 339500, 54805, 9960, 91404, 145781, 100820829, 84542,
2176, 51684, 9513, 7473, 4666, 23150, 5915, 5062, 4016, 23039,
159686, 1839, 5201, 93166, 64753, 29959, 5496, 23245, 5069, 56916,
92344, 23092, 3992, 9415, 10554, 7456, 9570, 57178, 23143, 161176,
5424, 2034, 10277, 11278, 79803, 6653, 4756, 132660, 5430, 9031,
57158, 285761, 8110, 387700, 1829, 4126, 7323, 51308, 7332, 6598,
3757, 6187, 6660, 10529, 6920, 115286, 8451, 8943, 4137, 7514,
4801, 9709, 23177, 8671, 29915, 26207, 3680, 490, 493856, 58489,
54897, 4625, 22955, 84952, 10221, 2263, 84641, 4892, 1026, 84650,
8382, 221656, 2969, 144453, 117177, 2626, 8476, 161882, 51807, 4624,
9612, 55795, 51043, 144348, 51232, 8729, 3899, 11155, 23316, 79006,
146330, 6403, 6331, 4300, 427, 845, 3313, 113622, 5789, 376132,
29841, 8462, 203859, 401397, 5506, 55521, 5819, 4059, 6934, 57727,
23066, 79568, 83478, 10087, 9586, 222194, 10466, 10499, 58499, 79720,
4772, 10794, 125919, 602, 27332, 59345, 340359, 3705, 64710, 57801,
10818, 143684, 149281, 55013, 23095, 93649, 84034, 23347, 440926, 11124,
23293, 51426, 832, 7068, 57646, 152002, 7531, 1398, 100101267, 221937,
26873, 3709, 10576, 27040, 201176, 284403, 307, 6525, 5387, 1808,
114907, 1952, 4091, 5096, 23451, 51207, 142891, 4084, 3797, 1185,
5334, 217, 2042, 7781, 253461, 152404, 2992, 153478, 6695, 26249,
662, 23036, 22852, 23411, 23387, 6786, 7690, 10021, 8925, 5595,
63892, 23466, 11149, 139411, 755, 55347, 22820, 55111, 84444, 6258,
57623, 1387, 3762, 5861, 3339, 114822, 129787, 196528, 54437, 10395,
4023, 5095, 894, 3156, 1877, 283871, 2673, 23157, 10512, 2258,
79750, 84665, 26091, 5978, 8751, 79695, 2768, 155382, 84163, 7709,
4092, 1837, 2064, 4629, 55122, 8882, 150962, 23013, 8742, 489,
201134, 55114, 84264, 64428, 4089, 861, 7422
) %>% as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM AF Genes) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "AF-Associated Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
############ AF GENES — 0.5 µM #######################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- AF Entrez IDs -----------------
entrez_ids <- c(
3425, 53834, 9464, 79658, 55876, 3781, 9472, 11017, 64395, 23353,
57794, 79591, 2702, 51306, 857, 10418, 9644, 115509, 89797, 10728,
282996, 8497, 5695, 9026, 57666, 143384, 5883, 90102, 84909, 221037,
29119, 79933, 391, 81575, 8738, 5396, 26010, 744, 56981, 80724,
5781, 6910, 4598, 79068, 2250, 56978, 3752, 5208, 9736, 26778,
80315, 64374, 79159, 11078, 5308, 3782, 10654, 79776, 6310, 8082,
2995, 23224, 79741, 5708, 26136, 9967, 84033, 9148, 29982, 81488,
1788, 9208, 2908, 1021, 5339, 5727, 23118, 10090, 57585, 221154,
1073, 29998, 817, 858, 3486, 5747, 2104, 55105, 4194, 3570,
1607, 6586, 5570, 2070, 27145, 4646, 3159, 9748, 79991, 221035,
5033, 80212, 196385, 79600, 11046, 54014, 151636, 64778, 92597, 3983,
7090, 9267, 7273, 26018, 9948, 2131, 55870, 5523, 5318, 6239,
3480, 55777, 2066, 11165, 2028, 57619, 2690, 23414, 55818, 84700,
28965, 80204, 463, 5108, 222553, 387119, 28981, 11113, 10301, 995,
23030, 2697, 996, 339500, 54805, 9960, 91404, 145781, 100820829, 84542,
2176, 51684, 9513, 7473, 4666, 23150, 5915, 5062, 4016, 23039,
159686, 1839, 5201, 93166, 64753, 29959, 5496, 23245, 5069, 56916,
92344, 23092, 3992, 9415, 10554, 7456, 9570, 57178, 23143, 161176,
5424, 2034, 10277, 11278, 79803, 6653, 4756, 132660, 5430, 9031,
57158, 285761, 8110, 387700, 1829, 4126, 7323, 51308, 7332, 6598,
3757, 6187, 6660, 10529, 6920, 115286, 8451, 8943, 4137, 7514,
4801, 9709, 23177, 8671, 29915, 26207, 3680, 490, 493856, 58489,
54897, 4625, 22955, 84952, 10221, 2263, 84641, 4892, 1026, 84650,
8382, 221656, 2969, 144453, 117177, 2626, 8476, 161882, 51807, 4624,
9612, 55795, 51043, 144348, 51232, 8729, 3899, 11155, 23316, 79006,
146330, 6403, 6331, 4300, 427, 845, 3313, 113622, 5789, 376132,
29841, 8462, 203859, 401397, 5506, 55521, 5819, 4059, 6934, 57727,
23066, 79568, 83478, 10087, 9586, 222194, 10466, 10499, 58499, 79720,
4772, 10794, 125919, 602, 27332, 59345, 340359, 3705, 64710, 57801,
10818, 143684, 149281, 55013, 23095, 93649, 84034, 23347, 440926, 11124,
23293, 51426, 832, 7068, 57646, 152002, 7531, 1398, 100101267, 221937,
26873, 3709, 10576, 27040, 201176, 284403, 307, 6525, 5387, 1808,
114907, 1952, 4091, 5096, 23451, 51207, 142891, 4084, 3797, 1185,
5334, 217, 2042, 7781, 253461, 152404, 2992, 153478, 6695, 26249,
662, 23036, 22852, 23411, 23387, 6786, 7690, 10021, 8925, 5595,
63892, 23466, 11149, 139411, 755, 55347, 22820, 55111, 84444, 6258,
57623, 1387, 3762, 5861, 3339, 114822, 129787, 196528, 54437, 10395,
4023, 5095, 894, 3156, 1877, 283871, 2673, 23157, 10512, 2258,
79750, 84665, 26091, 5978, 8751, 79695, 2768, 155382, 84163, 7709,
4092, 1837, 2064, 4629, 55122, 8882, 150962, 23013, 8742, 489,
201134, 55114, 84264, 64428, 4089, 861, 7422
) %>% as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM AF Genes) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "AF-Associated Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Proportion of Heart Failure GWAS genes across corrmotifs (per concentration)
# ----------------- Load Required Libraries -----------------
library(dplyr)
library(ggplot2)
# ----------------- Heart Failure Entrez IDs -----------------
hf_entrez_ids <- c(
9709, 8882, 4023, 29959, 5496, 3992, 9415, 5308, 1026, 54437, 79068, 10221,
9031, 1187, 1952, 3705, 84722, 7273, 23293, 155382, 9531, 602, 27258, 84163,
81846, 79933, 56911, 64753, 93210, 1021, 283450, 5998, 57602, 114991, 7073,
3156, 100101267, 22996, 285025, 11080, 11124, 54810, 7531, 27241, 4774, 57794,
463, 91319, 6598, 9640, 2186, 26010, 80816, 571, 88, 51652, 64788, 90523, 2969,
7781, 80777, 10725, 23387, 817, 134728, 8842, 949, 6934, 129787, 10327, 202052,
2318, 5578, 6801, 6311, 10019, 80724, 217, 84909, 388591, 55101, 9839, 27161,
5310, 387119, 4641, 5587, 55188, 222553, 9960, 22852, 10087, 9570, 54497,
200942, 26249, 4137, 375056, 5409, 64116, 8291, 22876, 339855, 4864, 5142,
221692, 55023, 51426, 6146, 84251, 8189, 27332, 57099, 1869, 1112, 23327,
11264, 6001
) %>% as.character()
# ----------------- Load CorrMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorrMotif Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Category = ifelse(Entrez_ID %in% hf_entrez_ids, "HF Genes", "Non-HF Genes"),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Category = ifelse(Entrez_ID %in% hf_entrez_ids, "HF Genes", "Non-HF Genes"),
Concentration = "0.5 µM"
)
# ----------------- Combine Data -----------------
df_combined <- bind_rows(df_0.1, df_0.5)
df_combined$Category <- factor(df_combined$Category, levels = c("HF Genes", "Non-HF Genes"))
# ----------------- Calculate Proportions -----------------
proportion_data <- df_combined %>%
group_by(Concentration, Response_Group, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Concentration, Response_Group) %>%
mutate(Percentage = Count / sum(Count) * 100)
# ----------------- Chi-square or Fisher's Test -----------------
get_chi_or_fisher <- function(df, ref_group) {
ref_counts <- df %>%
ungroup() %>%
filter(Response_Group == ref_group) %>%
{ ref_tbl <- .[ , c("Category", "Count")]; setNames(ref_tbl$Count, ref_tbl$Category) }
df %>%
filter(Response_Group != ref_group) %>%
group_by(Response_Group) %>%
summarise(
p_value = {
group_counts <- Count[Category %in% c("HF Genes", "Non-HF Genes")]
if (length(group_counts) < 2) group_counts <- c(group_counts, 0)
contingency_table <- matrix(c(
group_counts[1], group_counts[2],
ref_counts["HF Genes"], ref_counts["Non-HF Genes"]
), nrow = 2)
if (any(contingency_table < 5)) {
fisher.test(contingency_table)$p.value
} else {
chisq.test(contingency_table)$p.value
}
},
.groups = "drop"
) %>%
mutate(Significance = ifelse(!is.na(p_value) & p_value < 0.05, "*", ""))
}
# ----------------- Run test -----------------
chi_0.1 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.1 µM"), "Non response")Warning: There was 1 warning in `summarise()`.
ℹ In argument: `p_value = { ... }`.
ℹ In group 1: `Response_Group = "CX-DOX mid-late response"`.
Caused by warning in `chisq.test()`:
! Chi-squared approximation may be incorrectchi_0.5 <- get_chi_or_fisher(proportion_data %>% filter(Concentration == "0.5 µM"), "Non response")
chi_all <- bind_rows(
chi_0.1 %>% mutate(Concentration = "0.1 µM"),
chi_0.5 %>% mutate(Concentration = "0.5 µM")
)
proportion_data <- left_join(proportion_data, chi_all, by = c("Concentration", "Response_Group"))
# ----------------- Factor Order and Star Label -----------------
proportion_data$Response_Group <- factor(proportion_data$Response_Group, levels = c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
))
proportion_data$Concentration <- factor(proportion_data$Concentration, levels = c("0.1 µM", "0.5 µM"))
label_data <- proportion_data %>%
group_by(Concentration, Response_Group) %>%
summarise(Significance = dplyr::first(Significance), .groups = "drop") %>%
filter(!is.na(Significance) & Significance != "") %>%
mutate(y_pos = 105)
# ----------------- Final Plot -----------------
ggplot(proportion_data, aes(x = Response_Group, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = Significance),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~ Concentration, scales = "free_x") +
scale_fill_manual(values = c("HF Genes" = "#E41A1C", "Non-HF Genes" = "#377EB8")) +
scale_y_continuous(limits = c(0, 110), expand = c(0, 0)) +
labs(
title = "Heart Failure Gene Proportions Across\nCormotif Clusters (0.1 and 0.5 µM)",
x = "Response Groups", y = "Percentage of Genes", fill = "Gene Category"
) +
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(size = 16, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
axis.title.y = element_text(size = 14, face = "bold"),
axis.text.x = element_text(size = 11, angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
legend.title = element_text(size = 13),
legend.text = element_text(size = 12),
strip.text = element_text(size = 14, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
panel.spacing = unit(1.2, "lines")
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of Heart Failure GWAS genes across corrmotifs (per concentration)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Heart Failure Entrez IDs -----------------
entrez_ids <- c(
9709, 8882, 4023, 29959, 5496, 3992, 9415, 5308, 1026, 54437, 79068, 10221,
9031, 1187, 1952, 3705, 84722, 7273, 23293, 155382, 9531, 602, 27258, 84163,
81846, 79933, 56911, 64753, 93210, 1021, 283450, 5998, 57602, 114991, 7073,
3156, 100101267, 22996, 285025, 11080, 11124, 54810, 7531, 27241, 4774, 57794,
463, 91319, 6598, 9640, 2186, 26010, 80816, 571, 88, 51652, 64788, 90523, 2969,
7781, 80777, 10725, 23387, 817, 134728, 8842, 949, 6934, 129787, 10327, 202052,
2318, 5578, 6801, 6311, 10019, 80724, 217, 84909, 388591, 55101, 9839, 27161,
5310, 387119, 4641, 5587, 55188, 222553, 9960, 22852, 10087, 9570, 54497,
200942, 26249, 4137, 375056, 5409, 64116, 8291, 22876, 339855, 4864, 5142,
221692, 55023, 51426, 6146, 84251, 8189, 27332, 57099, 1869, 1112, 23327,
11264, 6001
) %>% as.character()
# ----------------- Load CorMotif Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate Response Groups -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load and Aggregate DEG Data -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
)
)
deg_mean <- deg_all %>%
group_by(Entrez_ID, Concentration) %>%
summarise(logFC = mean(logFC, na.rm = TRUE), .groups = "drop")
# ----------------- Merge and Annotate -----------------
hf_logfc <- inner_join(df_combined, deg_mean, by = c("Entrez_ID", "Concentration")) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Group = paste(Response_Group, Concentration)
)
# ----------------- Define Group Order -----------------
group_levels <- c(
"Non response",
"CX-DOX mid-late response",
"DOX-specific response",
"DOX only mid-late response",
"CX total + DOX early response",
"DOX early + CX-DOX mid-late response"
)
hf_logfc$Response_Group <- factor(hf_logfc$Response_Group, levels = group_levels)
# ----------------- Wilcoxon Test -----------------
comparison_map <- list(
"CX-DOX mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX only mid-late response 0.1 µM" = "Non response 0.1 µM",
"DOX-specific response 0.5 µM" = "Non response 0.5 µM",
"DOX only mid-late response 0.5 µM" = "Non response 0.5 µM",
"CX total + DOX early response 0.5 µM" = "Non response 0.5 µM",
"DOX early + CX-DOX mid-late response 0.5 µM" = "Non response 0.5 µM"
)
wilcoxon_results <- lapply(names(comparison_map), function(resp_group) {
control_group <- comparison_map[[resp_group]]
resp_vals <- hf_logfc$logFC[hf_logfc$Group == resp_group]
control_vals <- hf_logfc$logFC[hf_logfc$Group == control_group]
if (length(resp_vals) >= 2 && length(control_vals) >= 2) {
test_result <- wilcox.test(resp_vals, control_vals)
pval <- test_result$p.value
label <- case_when(
pval < 0.001 ~ "***",
pval < 0.01 ~ "**",
pval < 0.05 ~ "*",
TRUE ~ ""
)
y_pos <- max(resp_vals, na.rm = TRUE) + 0.5
return(data.frame(Group = resp_group, y_pos = y_pos, label = label, P_Value = signif(pval, 4)))
}
return(NULL)
}) %>% bind_rows()
label_data <- wilcoxon_results %>%
separate(Group, into = c("Response_Group", "Concentration"), sep = " (?=0\\.)") %>%
mutate(Response_Group = factor(Response_Group, levels = group_levels))
# ----------------- Plot -----------------
ggplot(hf_logfc, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 5, color = "black") +
facet_wrap(~Concentration, scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Mean logFC of Heart Failure Genes\nAcross CorMotif Response Groups",
x = "Response Group",
y = "Mean Log Fold Change",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of Heart Failure GWAS genes across corrmotifs (Faceted by concentration × timepoint)
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Heart Failure Entrez IDs -----------------
entrez_ids <- c(
9709, 8882, 4023, 29959, 5496, 3992, 9415, 5308, 1026, 54437, 79068, 10221,
9031, 1187, 1952, 3705, 84722, 7273, 23293, 155382, 9531, 602, 27258, 84163,
81846, 79933, 56911, 64753, 93210, 1021, 283450, 5998, 57602, 114991, 7073,
3156, 100101267, 22996, 285025, 11080, 11124, 54810, 7531, 27241, 4774, 57794,
463, 91319, 6598, 9640, 2186, 26010, 80816, 571, 88, 51652, 64788, 90523, 2969,
7781, 80777, 10725, 23387, 817, 134728, 8842, 949, 6934, 129787, 10327, 202052,
2318, 5578, 6801, 6311, 10019, 80724, 217, 84909, 388591, 55101, 9839, 27161,
5310, 387119, 4641, 5587, 55188, 222553, 9960, 22852, 10087, 9570, 54497,
200942, 26249, 4137, 375056, 5409, 64116, 8291, 22876, 339855, 4864, 5142,
221692, 55023, 51426, 6146, 84251, 8189, 27332, 57099, 1869, 1112, 23327,
11264, 6001
) %>% as.character()
# ----------------- Load CorMotif Response Group Data -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Annotate CorMotif Group Membership -----------------
df_0.1 <- data.frame(Entrez_ID = unique(c(prob_1_0.1, prob_2_0.1, prob_3_0.1))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response"
),
Concentration = "0.1 µM"
)
df_0.5 <- data.frame(Entrez_ID = unique(c(prob_1_0.5, prob_2_0.5, prob_3_0.5, prob_4_0.5, prob_5_0.5))) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific response",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late response",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early response",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late response"
),
Concentration = "0.5 µM"
)
df_combined <- bind_rows(df_0.1, df_0.5)
# ----------------- Load DEG Files -----------------
deg_files <- list(
"CX_0.1_3" = "data/DEGs/Toptable_CX_0.1_3.csv",
"CX_0.1_24" = "data/DEGs/Toptable_CX_0.1_24.csv",
"CX_0.1_48" = "data/DEGs/Toptable_CX_0.1_48.csv",
"CX_0.5_3" = "data/DEGs/Toptable_CX_0.5_3.csv",
"CX_0.5_24" = "data/DEGs/Toptable_CX_0.5_24.csv",
"CX_0.5_48" = "data/DEGs/Toptable_CX_0.5_48.csv",
"DOX_0.1_3" = "data/DEGs/Toptable_DOX_0.1_3.csv",
"DOX_0.1_24" = "data/DEGs/Toptable_DOX_0.1_24.csv",
"DOX_0.1_48" = "data/DEGs/Toptable_DOX_0.1_48.csv",
"DOX_0.5_3" = "data/DEGs/Toptable_DOX_0.5_3.csv",
"DOX_0.5_24" = "data/DEGs/Toptable_DOX_0.5_24.csv",
"DOX_0.5_48" = "data/DEGs/Toptable_DOX_0.5_48.csv"
)
deg_all <- map2_dfr(deg_files, names(deg_files), ~{
read_csv(.x, show_col_types = FALSE) %>%
mutate(Condition = .y)
}) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Concentration = case_when(
str_detect(Condition, "0.1") ~ "0.1 µM",
str_detect(Condition, "0.5") ~ "0.5 µM"
),
Timepoint = factor(case_when(
str_detect(Condition, "_3$") ~ "3 hr",
str_detect(Condition, "_24$") ~ "24 hr",
str_detect(Condition, "_48$") ~ "48 hr"
), levels = c("3 hr", "24 hr", "48 hr"))
)
# ----------------- Merge and Annotate -----------------
hf_all <- inner_join(df_combined, deg_all, by = c("Entrez_ID", "Concentration")) %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, keys = Entrez_ID,
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Response_Group = factor(Response_Group, levels = c(
"Non response", "CX-DOX mid-late response", "DOX-specific response",
"DOX only mid-late response", "CX total + DOX early response", "DOX early + CX-DOX mid-late response"
))
)
# ----------------- Wilcoxon Tests -----------------
comparison_groups <- levels(hf_all$Response_Group)[-1]
stat_results <- list()
for (conc in unique(hf_all$Concentration)) {
for (tp in levels(hf_all$Timepoint)) {
sub <- hf_all %>% filter(Concentration == conc, Timepoint == tp)
for (grp in comparison_groups) {
if (grp %in% unique(sub$Response_Group)) {
test <- tryCatch({
wilcox.test(
sub$logFC[sub$Response_Group == grp],
sub$logFC[sub$Response_Group == "Non response"]
)
}, error = function(e) NULL)
if (!is.null(test) && !is.nan(test$p.value)) {
stat_results[[length(stat_results) + 1]] <- data.frame(
Response_Group = grp,
Concentration = conc,
Timepoint = tp,
p_value = signif(test$p.value, 3),
label = case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
),
y_pos = max(sub$logFC[sub$Response_Group == grp], na.rm = TRUE) + 0.5
)
}
}
}
}
}
label_data <- bind_rows(stat_results) %>%
mutate(Response_Group = factor(Response_Group, levels = levels(hf_all$Response_Group)))
# ----------------- Plot -----------------
ggplot(hf_all, aes(x = Response_Group, y = logFC, fill = Response_Group)) +
geom_boxplot(outlier.size = 0.6, width = 0.7, na.rm = TRUE) +
geom_text(data = label_data, aes(x = Response_Group, y = y_pos, label = label),
inherit.aes = FALSE, size = 4, vjust = 0) +
facet_grid(rows = vars(factor(Timepoint, levels = c("3 hr", "24 hr", "48 hr"))),
cols = vars(Concentration), scales = "free_x") +
scale_fill_brewer(palette = "Set1") +
labs(
title = "Heart Failure Gene logFC by\nCorMotif Group, Timepoint, and Concentration",
x = "Response Group",
y = "logFC",
fill = "Response Group"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(hjust = 0.5, face = "bold", size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title = element_text(size = 14),
legend.title = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Mean logFC of Heart Failure GWAS genes across corrmotifs (Faceted by concentration × timepoint × drug (CX vs DOX side-by-side))
############ HEART FAILURE GENES — 0.1 µM #######################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Heart Failure Entrez IDs -----------------
entrez_ids <- c(
9709, 8882, 4023, 29959, 5496, 3992, 9415, 5308, 1026, 54437, 79068, 10221,
9031, 1187, 1952, 3705, 84722, 7273, 23293, 155382, 9531, 602, 27258, 84163,
81846, 79933, 56911, 64753, 93210, 1021, 283450, 5998, 57602, 114991, 7073,
3156, 100101267, 22996, 285025, 11080, 11124, 54810, 7531, 27241, 4774, 57794,
463, 91319, 6598, 9640, 2186, 26010, 80816, 571, 88, 51652, 64788, 90523, 2969,
7781, 80777, 10725, 23387, 817, 134728, 8842, 949, 6934, 129787, 10327, 202052,
2318, 5578, 6801, 6311, 10019, 80724, 217, 84909, 388591, 55101, 9839, 27161,
5310, 387119, 4641, 5587, 55188, 222553, 9960, 22852, 10087, 9570, 54497,
200942, 26249, 4137, 375056, 5409, 64116, 8291, 22876, 339855, 4864, 5142,
221692, 55023, 51426, 6146, 84251, 8189, 27332, 57099, 1869, 1112, 23327,
11264, 6001
) %>% as.character()
# ----------------- Load CorMotif Groups (0.1 µM) -----------------
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.1 µM DEG Files -----------------
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.1 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late\nresponse (0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late\nresponse (0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late\nresponse (0.1 µM)",
"DOX only mid-late\nresponse (0.1 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.1 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.1 µM HF Genes) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "Heart Failure Gene logFC by Drug and Response Group (0.1 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
############ HEART FAILURE GENES — 0.5 µM #######################
# ----------------- Load Libraries -----------------
library(tidyverse)
library(org.Hs.eg.db)
library(AnnotationDbi)
# ----------------- Heart Failure Entrez IDs -----------------
entrez_ids <- c(
9709, 8882, 4023, 29959, 5496, 3992, 9415, 5308, 1026, 54437, 79068, 10221,
9031, 1187, 1952, 3705, 84722, 7273, 23293, 155382, 9531, 602, 27258, 84163,
81846, 79933, 56911, 64753, 93210, 1021, 283450, 5998, 57602, 114991, 7073,
3156, 100101267, 22996, 285025, 11080, 11124, 54810, 7531, 27241, 4774, 57794,
463, 91319, 6598, 9640, 2186, 26010, 80816, 571, 88, 51652, 64788, 90523, 2969,
7781, 80777, 10725, 23387, 817, 134728, 8842, 949, 6934, 129787, 10327, 202052,
2318, 5578, 6801, 6311, 10019, 80724, 217, 84909, 388591, 55101, 9839, 27161,
5310, 387119, 4641, 5587, 55188, 222553, 9960, 22852, 10087, 9570, 54497,
200942, 26249, 4137, 375056, 5409, 64116, 8291, 22876, 339855, 4864, 5142,
221692, 55023, 51426, 6146, 84251, 8189, 27332, 57099, 1869, 1112, 23327,
11264, 6001
) %>% as.character()
# ----------------- Load CorMotif Groups (0.5 µM) -----------------
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Helper: Load and Label DEG -----------------
load_deg <- function(file, drug, time_hr) {
read.csv(file) %>%
mutate(
Drug = drug,
Timepoint = factor(time_hr, levels = c(3, 24, 48)),
Entrez_ID = as.character(Entrez_ID)
)
}
# ----------------- Load All 0.5 µM DEG Files -----------------
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Concentration = "0.5 µM",
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX total + DOX early\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX early + CX-DOX mid-late\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group)) %>%
filter(Entrez_ID %in% entrez_ids)
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX total + DOX early\nresponse (0.5 µM)",
"DOX early + CX-DOX mid-late\nresponse (0.5 µM)"
))
)
# ----------------- Wilcoxon Test (CX and DOX separate) -----------------
wilcox_data <- deg_0.5 %>%
group_by(Drug, Timepoint) %>%
group_modify(~{
df <- .x
ref <- df %>% filter(str_detect(Response_Group, "Non response"))
other <- df %>% filter(!str_detect(Response_Group, "Non response"))
if (nrow(ref) == 0 || nrow(other) == 0) return(tibble())
map_dfr(unique(other$Response_Group), function(grp) {
g_data <- other %>% filter(Response_Group == grp)
if (nrow(g_data) > 0) {
test <- tryCatch(
wilcox.test(g_data$logFC, ref$logFC),
error = function(e) NULL
)
tibble(
Response_Group = grp,
p_value = if (!is.null(test)) signif(test$p.value, 3) else NA,
label = if (!is.null(test)) case_when(
test$p.value < 0.001 ~ "***",
test$p.value < 0.01 ~ "**",
test$p.value < 0.05 ~ "*",
TRUE ~ ""
) else "",
y_pos = max(g_data$logFC, na.rm = TRUE) + 0.5
)
} else {
tibble()
}
})
}) %>%
ungroup()
# ----------------- Final Boxplot (0.5 µM HF Genes) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(0.75), outlier.size = 0.6, width = 0.7) +
geom_text(data = wilcox_data,
aes(x = Timepoint, y = y_pos, label = label, group = Drug),
position = position_dodge(0.75),
inherit.aes = FALSE,
size = 4, vjust = 0) +
facet_grid(. ~ Response_Group, scales = "free_y") +
scale_fill_manual(values = c("CX" = "blue", "DOX" = "red")) +
labs(
title = "Heart Failure Gene logFC by Drug and Response Group (0.5 µM)",
x = "Timepoint (hours)",
y = "logFC",
fill = "Drug"
) +
theme_bw(base_size = 14) +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| 01a423f | sayanpaul01 | 2025-05-15 |
📌 Corr-motif Boxplots
📌 Non response (0.1 micromolar)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group files
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the non-response group
target_entrez <- 92342 # Ensure this is from prob_1_0.1
# Stop if the gene is not in the non-response group
if (!(target_entrez %in% prob_1_0.1)) {
stop("Selected gene is not in the non-response group for 0.1 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTIREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.1 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.1")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.1")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) + # Only facets by Time now
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("Non response (0.1 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
| Version | Author | Date |
|---|---|---|
| aab6b9f | sayanpaul01 | 2025-04-07 |
📌 CX-DOX Mid-Late Response (0.1 micromolar)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group files
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the CX-DOX mid-late response group
target_entrez <- 4521 # Replace with your desired Entrez ID from prob_2_0.1
# Stop if the gene is not in the CX-DOX mid-late response group
if (!(target_entrez %in% prob_2_0.1)) {
stop("Selected gene is not in the CX-DOX mid-late response group for 0.1 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.1 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.1")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.1")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) + # Only facets by Time
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("CX-DOX mid-late\nresponse (0.1 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
| Version | Author | Date |
|---|---|---|
| aab6b9f | sayanpaul01 | 2025-04-07 |
📌 DOX-only mid-late response (0.1 micromolar)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group files
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the DOX-only mid-late response group
target_entrez <- 1674 # Replace with your desired Entrez ID from prob_3_0.1
# Stop if the gene is not in the DOX-only mid-late response group
if (!(target_entrez %in% prob_3_0.1)) {
stop("Selected gene is not in the DOX-only mid-late response group for 0.1 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- reshape2::melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.1 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.1")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.1")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("DOX-only mid-late\nresponse (0.1 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
| Version | Author | Date |
|---|---|---|
| aab6b9f | sayanpaul01 | 2025-04-07 |
📌 Non response (0.5 micromolar)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group: Non response (0.5 µM)
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the non-response group (0.5 µM)
target_entrez <- 9446 # Replace with your desired Entrez ID from prob_1_0.5
# Check if it's in the group
if (!(target_entrez %in% prob_1_0.5)) {
stop("Selected gene is not in the Non response group for 0.5 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.5 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("Non response (0.5 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
| Version | Author | Date |
|---|---|---|
| aab6b9f | sayanpaul01 | 2025-04-07 |
📌 DOX-specific response (0.5 µM)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group: DOX-specific response (0.5 µM)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the DOX-specific response group (0.5 µM)
target_entrez <- 114821 # Replace with your desired Entrez ID from prob_2_0.5
# Check membership
if (!(target_entrez %in% prob_2_0.5)) {
stop("Selected gene is not in the DOX-specific response group for 0.5 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.5 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("DOX-specific\nresponse (0.5 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
| Version | Author | Date |
|---|---|---|
| aab6b9f | sayanpaul01 | 2025-04-07 |
📌 DOX only mid-late response (0.5 µM)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group: DOX only mid-late response (0.5 µM)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the DOX only mid-late response group (0.5 µM)
target_entrez <- 4892 # Replace with your desired Entrez ID from prob_3_0.5
# Check membership
if (!(target_entrez %in% prob_3_0.5)) {
stop("Selected gene is not in the DOX only mid-late response group for 0.5 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.5 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("DOX only mid-late\nresponse (0.5 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
| Version | Author | Date |
|---|---|---|
| aab6b9f | sayanpaul01 | 2025-04-07 |
📌 CX total + DOX early response (0.5 micromolar)
# ----------------- Load Libraries -----------------
library(dplyr)
library(ggplot2)
library(reshape2)
# ----------------- Load Response Group -----------------
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
# ----------------- Load Expression Dataset -----------------
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# ----------------- Choose a Target Gene -----------------
target_entrez <- 100996485 # Replace this with any valid Entrez ID from prob_4_0.5
# Check if gene is valid
if (!(target_entrez %in% prob_4_0.5)) {
stop("Selected gene is not in the CX total + DOX early response group for 0.5 micromolar.")
}
# ----------------- Filter Data for Selected Gene -----------------
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if (nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# ----------------- Reshape to Long Format -----------------
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# ----------------- Extract Metadata -----------------
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# ----------------- Filter for 0.5 µM Only -----------------
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# ----------------- Convert to Factors -----------------
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# ----------------- Map Individual IDs -----------------
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# ----------------- Define Drug Color Palette -----------------
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# ----------------- Extract Gene Symbol -----------------
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# ----------------- Plot -----------------
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("CX total+ DOX early\nresponse (0.5 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
📌 DOX early + CX-DOX mid-late response (0.5 µM)
# Load libraries
library(dplyr)
library(ggplot2)
library(reshape2)
# Load response group: DOX early + CX-DOX mid-late response (0.5 µM)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# Load expression dataset
boxplot1 <- read.csv("data/boxplot1.csv", check.names = FALSE)
boxplot1 <- as.data.frame(boxplot1)
# Choose a gene from the DOX early + CX-DOX mid-late response group (0.5 µM)
target_entrez <- 672 # Replace with your desired Entrez ID from prob_5_0.5
# Check membership
if (!(target_entrez %in% prob_5_0.5)) {
stop("Selected gene is not in the DOX early + CX-DOX mid-late response group for 0.5 micromolar.")
}
# Filter for selected gene
gene_data <- boxplot1[boxplot1$ENTREZID == target_entrez, ]
if(nrow(gene_data) == 0) stop("No data found for selected ENTREZID.")
# Reshape to long format
gene_data_long <- melt(gene_data,
id.vars = c("ENTREZID", "SYMBOL", "GENENAME"),
variable.name = "Sample",
value.name = "log2CPM")
# Extract metadata from sample names
gene_data_long <- gene_data_long %>%
mutate(
Time = sub(".*_(\\d+)$", "\\1", Sample),
Concentration = sub(".*_(0\\.\\d)_\\d+$", "\\1", Sample),
Drug = sub(".*_(CX\\.5461|DOX|VEH)_.*", "\\1", Sample),
Indv = sub("^([0-9]+\\.[0-9]+)_.*", "\\1", Sample)
)
# Filter for 0.5 micromolar only
gene_data_long <- gene_data_long %>% filter(Concentration == "0.5")
# Convert to factors
gene_data_long$Time <- factor(gene_data_long$Time, levels = c("3", "24", "48"))
gene_data_long$Concentration <- factor(gene_data_long$Concentration, levels = "0.5")
# Map individual IDs
indv_mapping <- c("75.1" = "1", "78.1" = "2", "87.1" = "3",
"17.3" = "4", "84.1" = "5", "90.1" = "6")
gene_data_long <- gene_data_long %>%
mutate(Indv = ifelse(Indv %in% names(indv_mapping), indv_mapping[Indv], "Unknown"))
# Define color palette for drugs
drug_palette <- c("CX.5461" = "#08306B", "DOX" = "#E7298A", "VEH" = "green")
# Extract gene symbol for labeling
gene_symbol <- unique(gene_data_long$SYMBOL)[1]
# Create the boxplot
ggplot(gene_data_long, aes(x = Drug, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = drug_palette) +
facet_grid(. ~ Time, labeller = label_both) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5,
position = position_jitter(width = -0.3, height = 0)) +
ggtitle("DOX early+ CX-DOX mid-late\nresponse (0.5 micromolar)") +
labs(
x = "Drugs",
y = paste(gene_symbol, " log2CPM")
) +
ylim(0, NA) +
theme_bw() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.ticks = element_line(linewidth = 1.5),
axis.line = element_line(linewidth = 1.5),
axis.text.y = element_text(size = 10, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 45, hjust = 1),
strip.text = element_text(size = 12, face = "bold")
)
| Version | Author | Date |
|---|---|---|
| aab6b9f | sayanpaul01 | 2025-04-07 |
📌 Corrmotif conc boxplot for manuscript 0.1
# Load Required Libraries
library(dplyr)
library(ggplot2)
# ----------------- Load Response Groups -----------------
# 0.1 µM
prob_1_0.1 <- as.character(read.csv("data/prob_1_0.1.csv")$Entrez_ID)
prob_2_0.1 <- as.character(read.csv("data/prob_2_0.1.csv")$Entrez_ID)
prob_3_0.1 <- as.character(read.csv("data/prob_3_0.1.csv")$Entrez_ID)
# ----------------- Load DEG Data -----------------
# Helper function to load and label
load_deg <- function(path, drug, time) {
read.csv(path) %>%
mutate(Drug = drug, Timepoint = time)
}
# 0.1 µM DEG data
deg_0.1 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.1_3.csv", "CX.5461", 3),
load_deg("data/DEGs/Toptable_CX_0.1_24.csv", "CX.5461", 24),
load_deg("data/DEGs/Toptable_CX_0.1_48.csv", "CX.5461", 48),
load_deg("data/DEGs/Toptable_DOX_0.1_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.1_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.1_48.csv", "DOX", 48)
) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Response_Group = case_when(
Entrez_ID %in% prob_1_0.1 ~ "Non response\n(0.1 µM)",
Entrez_ID %in% prob_2_0.1 ~ "CX-DOX mid-late response\n(0.1 µM)",
Entrez_ID %in% prob_3_0.1 ~ "DOX only mid-late response\n(0.1 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# ----------------- Set Factor Levels -----------------
deg_0.1 <- deg_0.1 %>%
mutate(
Timepoint = factor(Timepoint, levels = c(3, 24, 48)),
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.1 µM)",
"CX-DOX mid-late response\n(0.1 µM)",
"DOX only mid-late response\n(0.1 µM)"
))
)
# ----------------- Plot Boxplot (Grouped by Timepoint, Faceted by Response Group) -----------------
ggplot(deg_0.1, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75)) +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
facet_grid(. ~ Response_Group, scales = "free_x") +
theme_bw() +
labs(
x = "Timepoint (hours)",
y = "Log Fold Change",
title = "Log Fold Change Distribution by Drug and Response Group (0.1 µM)",
fill = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| a568872 | sayanpaul01 | 2025-04-10 |
📌 Corrmotif conc boxplot for manuscript 0.5
# Load Required Libraries
library(dplyr)
library(ggplot2)
# ----------------- Load Response Groups -----------------
# 0.5 µM
prob_1_0.5 <- as.character(read.csv("data/prob_1_0.5.csv")$Entrez_ID)
prob_2_0.5 <- as.character(read.csv("data/prob_2_0.5.csv")$Entrez_ID)
prob_3_0.5 <- as.character(read.csv("data/prob_3_0.5.csv")$Entrez_ID)
prob_4_0.5 <- as.character(read.csv("data/prob_4_0.5.csv")$Entrez_ID)
prob_5_0.5 <- as.character(read.csv("data/prob_5_0.5.csv")$Entrez_ID)
# ----------------- Load DEG Data -----------------
# Helper function to load and label
load_deg <- function(path, drug, time) {
read.csv(path) %>%
mutate(Drug = drug, Timepoint = time)
}
# 0.5 µM DEG data
deg_0.5 <- bind_rows(
load_deg("data/DEGs/Toptable_CX_0.5_3.csv", "CX.5461", 3),
load_deg("data/DEGs/Toptable_CX_0.5_24.csv", "CX.5461", 24),
load_deg("data/DEGs/Toptable_CX_0.5_48.csv", "CX.5461", 48),
load_deg("data/DEGs/Toptable_DOX_0.5_3.csv", "DOX", 3),
load_deg("data/DEGs/Toptable_DOX_0.5_24.csv", "DOX", 24),
load_deg("data/DEGs/Toptable_DOX_0.5_48.csv", "DOX", 48)
) %>%
mutate(
Entrez_ID = as.character(Entrez_ID),
Response_Group = case_when(
Entrez_ID %in% prob_1_0.5 ~ "Non response\n(0.5 µM)",
Entrez_ID %in% prob_2_0.5 ~ "DOX-specific\nresponse (0.5 µM)",
Entrez_ID %in% prob_3_0.5 ~ "DOX only mid-late\nresponse (0.5 µM)",
Entrez_ID %in% prob_4_0.5 ~ "CX + DOX (early)\nresponse (0.5 µM)",
Entrez_ID %in% prob_5_0.5 ~ "DOX + CX (mid-late)\nresponse (0.5 µM)",
TRUE ~ NA_character_
)
) %>%
filter(!is.na(Response_Group))
# ----------------- Set Factor Levels -----------------
deg_0.5 <- deg_0.5 %>%
mutate(
Timepoint = factor(Timepoint, levels = c(3, 24, 48)),
Response_Group = factor(Response_Group, levels = c(
"Non response\n(0.5 µM)",
"DOX-specific\nresponse (0.5 µM)",
"DOX only mid-late\nresponse (0.5 µM)",
"CX + DOX (early)\nresponse (0.5 µM)",
"DOX + CX (mid-late)\nresponse (0.5 µM)"
))
)
# ----------------- Plot Boxplot (Grouped by Timepoint, Faceted by Response Group) -----------------
ggplot(deg_0.5, aes(x = Timepoint, y = logFC, fill = Drug)) +
geom_boxplot(position = position_dodge(width = 0.75)) +
scale_fill_manual(values = c("CX.5461" = "blue", "DOX" = "red")) +
facet_grid(. ~ Response_Group, scales = "free_x") +
theme_bw() +
labs(
x = "Timepoint (hours)",
y = "Log Fold Change",
title = "Log Fold Change Distribution by Drug and Response Group (0.5 µM)",
fill = "Drug"
) +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 11, face = "bold"),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)
)
| Version | Author | Date |
|---|---|---|
| a568872 | sayanpaul01 | 2025-04-10 |
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] reshape2_1.4.4 lubridate_1.9.4 forcats_1.0.0
[4] stringr_1.5.1 purrr_1.0.4 readr_2.1.5
[7] tibble_3.2.1 tidyverse_2.0.0 org.Hs.eg.db_3.18.0
[10] AnnotationDbi_1.64.1 IRanges_2.36.0 S4Vectors_0.40.2
[13] tidyr_1.3.1 ggplot2_3.5.2 gprofiler2_0.2.3
[16] BiocParallel_1.36.0 dplyr_1.1.4 Rfast_2.1.5.1
[19] RcppParallel_5.1.10 zigg_0.0.2 Rcpp_1.0.12
[22] Cormotif_1.48.0 limma_3.58.1 affy_1.80.0
[25] Biobase_2.62.0 BiocGenerics_0.48.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-9 rlang_1.1.3
[4] magrittr_2.0.3 git2r_0.36.2 compiler_4.3.0
[7] RSQLite_2.3.9 getPass_0.2-4 png_0.1-8
[10] callr_3.7.6 vctrs_0.6.5 pkgconfig_2.0.3
[13] crayon_1.5.3 fastmap_1.2.0 XVector_0.42.0
[16] labeling_0.4.3 promises_1.3.2 rmarkdown_2.29
[19] tzdb_0.5.0 ps_1.8.1 preprocessCore_1.64.0
[22] bit_4.6.0 xfun_0.52 zlibbioc_1.48.2
[25] cachem_1.1.0 GenomeInfoDb_1.38.8 jsonlite_2.0.0
[28] blob_1.2.4 later_1.3.2 parallel_4.3.0
[31] R6_2.6.1 RColorBrewer_1.1-3 bslib_0.9.0
[34] stringi_1.8.3 jquerylib_0.1.4 knitr_1.50
[37] timechange_0.3.0 httpuv_1.6.15 tidyselect_1.2.1
[40] rstudioapi_0.17.1 yaml_2.3.10 codetools_0.2-20
[43] processx_3.8.6 plyr_1.8.9 withr_3.0.2
[46] KEGGREST_1.42.0 evaluate_1.0.3 Biostrings_2.70.3
[49] pillar_1.10.2 affyio_1.72.0 BiocManager_1.30.25
[52] whisker_0.4.1 plotly_4.10.4 generics_0.1.3
[55] vroom_1.6.5 rprojroot_2.0.4 RCurl_1.98-1.17
[58] hms_1.1.3 munsell_0.5.1 scales_1.3.0
[61] glue_1.7.0 lazyeval_0.2.2 tools_4.3.0
[64] data.table_1.17.0 fs_1.6.3 grid_4.3.0
[67] colorspace_2.1-0 GenomeInfoDbData_1.2.11 cli_3.6.1
[70] viridisLite_0.4.2 gtable_0.3.6 sass_0.4.10
[73] digest_0.6.34 htmlwidgets_1.6.4 farver_2.1.2
[76] memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.4
[79] httr_1.4.7 statmod_1.5.0 bit64_4.6.0-1