Corrmotif Time

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Last updated: 2025-03-03

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📌 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 CX 0.1 (3hr, 24hr and 48hr)

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 CX-5461 (0.1 µM) and Vehicle (0.1 µM) across 3h, 24h, and 48h
exprs.corrmotif <- as.matrix(Corrmotif_df %>%                      dplyr::select(matches("CX_0.1_3|CX_0.1_24|CX_0.1_48|VEH_0.1_3|VEH_0.1_24|VEH_0.1_48")))


# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- groupid %>%
  dplyr::select(matches("CX_0.1_3|CX_0.1_24|CX_0.1_48|VEH_0.1_3|VEH_0.1_24|VEH_0.1_48"))

compid <- read.csv("data/Corrmotif/Compid.csv")
compid_df <- compid %>%
  filter(Cond1 %in% unique(as.numeric(unlist(groupid_df))) &
           Cond2 %in% unique(as.numeric(unlist(groupid_df))))

📌 Corrmotif Model CX 0.1 (3hr, 24hr and 48hr) (K=1:8)

set.seed(11111)
motif.fitted_CX_0.1 <- cormotiffit(exprs=exprs.corrmotif,
                            groupid=groupid_df, compid=compid_df,
                            K=1:8, max.iter=1000, BIC=TRUE, runtype="logCPM")

gene_prob_CX_0.1 <- motif.fitted_CX_0.1$bestmotif$p.post
rownames(gene_prob_CX_0.1) <- rownames(Corrmotif_df)
motif_prob_CX_0.1 <- motif.fitted_CX_0.1$bestmotif$clustlike
rownames(motif_prob_CX_0.1) <- rownames(gene_prob_CX_0.1)
write.csv(motif_prob_CX_0.1,"data/motif_prob_CX_0.1.csv")

📌 Plot motif CX 0.1 (3hr, 24hr and 48hr)

cormotif_CX_0.1 <- readRDS("data/Corrmotif/cormotif_CX_0.1.RDS")

cormotif_CX_0.1$bic

     K      bic
[1,] 1 119522.2
[2,] 2 117415.3
[3,] 3 117455.6
[4,] 4 117495.8
[5,] 5 117536.1
[6,] 6 117576.4
[7,] 7 117620.6
[8,] 8 117660.9

plotIC(cormotif_CX_0.1)

Past versions of Model_CX_0.1-1.png

Version	Author	Date
177fd18	sayanpaul01	2025-02-28

plotMotif(cormotif_CX_0.1)

Past versions of Model_CX_0.1-2.png

Version	Author	Date
177fd18	sayanpaul01	2025-02-28

📌 Distribution of Gene Clusters Identified by Corrmotif (CX 0.1 (3hr, 24hr and 48hr))

# Load necessary libraries
library(ggplot2)

# Data
data <- data.frame(
  Category = c("CX_low_non response", "CX_low_mid_late_response"),
  Value = c(13938, 341)
)

# 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("#66B2FF", "#99FF99"),
  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 (CX 0.1 micromolar)", x = NULL, y = NULL) +
  theme_void() +
  scale_fill_manual(values = custom_colors)  # Corrected color mapping

Past versions of pie-chart_CX_0.1-1.png

Version	Author	Date
867944a	sayanpaul01	2025-02-28

Extract Gene Probabilities (CX 0.1 (3hr, 24hr and 48hr))

# Extract posterior probabilities for genes
gene_prob_CX_0.1 <- cormotif_CX_0.1$bestmotif$p.post

rownames(gene_prob_CX_0.1) <- rownames(Corrmotif_df)


prob_CX_1_0.1  <- rownames(gene_prob_CX_0.1[(gene_prob_CX_0.1[,1] <0.5 & gene_prob_CX_0.1[,2] <0.5 & gene_prob_CX_0.1[,3]<0.5),])
length(prob_CX_1_0.1)

[1] 13938

prob_CX_2_0.1  <- rownames(gene_prob_CX_0.1[(gene_prob_CX_0.1[,1] <0.5 & gene_prob_CX_0.1[,2] >0.5 & gene_prob_CX_0.1[,3]>0.5),])
length(prob_CX_2_0.1)

[1] 341

📌 Corrmotif Model CX 0.5 (3hr, 24hr and 48hr)

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 CX-5461 (0.5 µM) and Vehicle (0.5 µM) across 3h, 24h, and 48h
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("CX_0.5_3|CX_0.5_24|CX_0.5_48|VEH_0.5_3|VEH_0.5_24|VEH_0.5_48", colnames(Corrmotif_df))])



# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- groupid %>%
  dplyr::select(matches("CX_0.5_3|CX_0.5_24|CX_0.5_48|VEH_0.5_3|VEH_0.5_24|VEH_0.5_48", 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 CX 0.5 (3hr, 24hr and 48hr) (K=1:8)

set.seed(11111)
motif.fitted_CX_0.5 <- cormotiffit(exprs=exprs.corrmotif,
                            groupid=groupid_df, compid=compid_df,
                            K=1:8, max.iter=1000, BIC=TRUE, runtype="logCPM")

gene_prob_CX_0.5 <- motif.fitted_CX_0.5$bestmotif$p.post
rownames(gene_prob_CX_0.5) <- rownames(Corrmotif_df)
motif_prob_CX_0.5 <- motif.fitted_CX_0.5$bestmotif$clustlike
rownames(motif_prob_CX_0.5) <- rownames(gene_prob_CX_0.5)
write.csv(motif_prob_CX_0.5,"data/motif_prob_CX_0.5.csv")

📌 Plot motif CX 0.5 (3hr, 24hr and 48hr)

cormotif_CX_0.5 <- readRDS("data/Corrmotif/cormotif_CX_0.5.RDS")

cormotif_CX_0.5$bic

     K      bic
[1,] 1 124024.3
[2,] 2 121807.6
[3,] 3 121847.9
[4,] 4 121888.1
[5,] 5 121928.4
[6,] 6 121968.6
[7,] 7 122012.9
[8,] 8 122053.1

plotIC(cormotif_CX_0.5)

Past versions of Model_CX_0.5-1.png

Version	Author	Date
177fd18	sayanpaul01	2025-02-28

plotMotif(cormotif_CX_0.5)

Past versions of Model_CX_0.5-2.png

Version	Author	Date
177fd18	sayanpaul01	2025-02-28

📌 Distribution of Gene Clusters Identified by Corrmotif (CX 0.5 (3hr, 24hr and 48hr))

# Load necessary libraries
library(ggplot2)

# Data
data <- data.frame(
  Category = c("CX_high_non response", "CX_high_mid_late_response"),
  Value = c(13892, 385)
)

# 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("#66B2FF", "#99FF99"),
  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 (CX 0.5 micromolar)", x = NULL, y = NULL) +
  theme_void() +
  scale_fill_manual(values = custom_colors)  # Corrected color mapping

Past versions of pie-chart_CX_0.5-1.png

Version	Author	Date
867944a	sayanpaul01	2025-02-28

Extract Gene Probabilities (CX 0.5 (3hr, 24hr and 48hr))

# Extract posterior probabilities for genes
gene_prob_CX_0.5 <- cormotif_CX_0.5$bestmotif$p.post

rownames(gene_prob_CX_0.5) <- rownames(Corrmotif_df)


prob_CX_1_0.5  <- rownames(gene_prob_CX_0.5[(gene_prob_CX_0.5[,1] <0.5 & gene_prob_CX_0.5[,2] <0.5 & gene_prob_CX_0.5[,3]<0.5),])
length(prob_CX_1_0.5)

[1] 13892

prob_CX_2_0.5  <- rownames(gene_prob_CX_0.5[(gene_prob_CX_0.5[,1] <0.5 & gene_prob_CX_0.5[,2] >0.5 & gene_prob_CX_0.5[,3]>0.5),])
length(prob_CX_2_0.5)

[1] 385

📌 Corrmotif Model DOX 0.1 (3hr, 24hr and 48hr)

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 DOX (0.1 µM) and Vehicle (0.1 µM) across 3h, 24h, and 48h
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("DOX_0.1_3|DOX_0.1_24|DOX_0.1_48|VEH_0.1_3|VEH_0.1_24|VEH_0.1_48", colnames(Corrmotif_df))])

# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- groupid %>%
  dplyr::select(matches("DOX_0.1_3|DOX_0.1_24|DOX_0.1_48|VEH_0.1_3|VEH_0.1_24|VEH_0.1_48", 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 DOX 0.1 (3hr, 24hr and 48hr) (K=1:8)

set.seed(11111)
motif.fitted_DOX_0.1 <- cormotiffit(exprs=exprs.corrmotif,
                            groupid=groupid_df, compid=compid_df,
                            K=1:8, max.iter=1000, BIC=TRUE, runtype="logCPM")

gene_prob_DOX_0.1 <- motif.fitted_DOX_0.1$bestmotif$p.post
rownames(gene_prob_DOX_0.1) <- rownames(Corrmotif_df)
motif_prob_DOX_0.1 <- motif.fitted_DOX_0.1$bestmotif$clustlike
rownames(motif_prob_DOX_0.1) <- rownames(gene_prob_DOX_0.1)
write.csv(motif_prob_DOX_0.1,"data/motif_prob_DOX_0.1.csv")

📌 Plot motif DOX 0.1 (3hr, 24hr and 48hr)

cormotif_DOX_0.1 <- readRDS("data/Corrmotif/cormotif_DOX_0.1.RDS")

cormotif_DOX_0.1$bic

     K      bic
[1,] 1 172174.3
[2,] 2 167341.3
[3,] 3 167385.4
[4,] 4 167421.8
[5,] 5 167462.0
[6,] 6 167502.3
[7,] 7 167542.5
[8,] 8 167582.8

plotIC(cormotif_DOX_0.1)

Past versions of Model_DOX_0.1-1.png

Version	Author	Date
177fd18	sayanpaul01	2025-02-28

plotMotif(cormotif_DOX_0.1)

Past versions of Model_DOX_0.1-2.png

Version	Author	Date
177fd18	sayanpaul01	2025-02-28

📌 Distribution of Gene Clusters Identified by Corrmotif (DOX 0.1 (3hr, 24hr and 48hr))

# Load necessary libraries
library(ggplot2)

# Data
data <- data.frame(
  Category = c("DOX_low_non response", "DOX_low_mid_late_response"),
  Value = c(12179, 2099)
)

# 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("#66B2FF", "#99FF99"),
  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 (DOX 0.1 micromolar)", x = NULL, y = NULL) +
  theme_void() +
  scale_fill_manual(values = custom_colors)  # Corrected color mapping

Past versions of pie-chart_DOX_0.1-1.png

Version	Author	Date
867944a	sayanpaul01	2025-02-28

Extract Gene Probabilities (DOX 0.1 (3hr, 24hr and 48hr))

# Extract posterior probabilities for genes
gene_prob_DOX_0.1 <- cormotif_DOX_0.1$bestmotif$p.post

rownames(gene_prob_DOX_0.1) <- rownames(Corrmotif_df)


prob_DOX_1_0.1  <- rownames(gene_prob_DOX_0.1[(gene_prob_DOX_0.1[,1] <0.5 & gene_prob_DOX_0.1[,2] <0.5 & gene_prob_DOX_0.1[,3]<0.5),])
length(prob_DOX_1_0.1)

[1] 12179

prob_DOX_2_0.1  <- rownames(gene_prob_DOX_0.1[(gene_prob_DOX_0.1[,1] <0.5 & gene_prob_DOX_0.1[,2] >0.5 & gene_prob_DOX_0.1[,3]>0.5),])
length(prob_DOX_2_0.1)

[1] 2099

📌 Corrmotif Model DOX 0.5 (3hr, 24hr and 48hr)

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 DOX (0.5 µM) and Vehicle (0.5 µM) across 3h, 24h, and 48h
exprs.corrmotif <- as.matrix(Corrmotif_df[, grep("DOX_0.5_3|DOX_0.5_24|DOX_0.5_48|VEH_0.5_3|VEH_0.5_24|VEH_0.5_48", colnames(Corrmotif_df))])



# Read group and comparison IDs
groupid <- read.csv("data/Corrmotif/groupid.csv")
groupid_df <- groupid %>%
  dplyr::select(matches("DOX_0.5_3|DOX_0.5_24|DOX_0.5_48|VEH_0.5_3|VEH_0.5_24|VEH_0.5_48", 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 DOX 0.5 (3hr, 24hr and 48hr) (K=1:8)

set.seed(11111)
motif.fitted_DOX_0.5 <- cormotiffit(exprs=exprs.corrmotif,
                            groupid=groupid_df, compid=compid_df,
                            K=1:8, max.iter=1000, BIC=TRUE, runtype="logCPM")

gene_prob_DOX_0.5 <- motif.fitted_DOX_0.5$bestmotif$p.post
rownames(gene_prob_DOX_0.5) <- rownames(Corrmotif_df)
motif_prob_DOX_0.5 <- motif.fitted_DOX_0.5$bestmotif$clustlike
rownames(motif_prob_DOX_0.5) <- rownames(gene_prob_DOX_0.5)
write.csv(motif_prob_DOX_0.5,"data/motif_prob_DOX_0.5.csv")

📌 Plot motif DOX 0.1 (3hr, 24hr and 48hr)

cormotif_DOX_0.5 <- readRDS("data/Corrmotif/cormotif_DOX_0.5.RDS")

cormotif_DOX_0.5$bic

     K      bic
[1,] 1 228116.5
[2,] 2 222769.9
[3,] 3 222778.8
[4,] 4 222820.9
[5,] 5 222861.0
[6,] 6 222901.2
[7,] 7 222943.0
[8,] 8 222981.5

plotIC(cormotif_DOX_0.5)

Past versions of Model_DOX_0.5-1.png

Version	Author	Date
177fd18	sayanpaul01	2025-02-28

plotMotif(cormotif_DOX_0.5)

Past versions of Model_DOX_0.5-2.png

Version	Author	Date
177fd18	sayanpaul01	2025-02-28

📌 Distribution of Gene Clusters Identified by Corrmotif (DOX 0.5 (3hr, 24hr and 48hr))

# Load necessary libraries
library(ggplot2)

# Data
data <- data.frame(
  Category = c("DOX_high_non response", "DOX_high_mid_late_response"),
  Value = c(7320, 6890)
)

# 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("#66B2FF", "#99FF99"),
  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 (DOX 0.5 micromolar)", x = NULL, y = NULL) +
  theme_void() +
  scale_fill_manual(values = custom_colors)  # Corrected color mapping

Past versions of pie-chart_DOX_0.5-1.png

Version	Author	Date
867944a	sayanpaul01	2025-02-28

Extract Gene Probabilities (DOX 0.5 (3hr, 24hr and 48hr))

# Extract posterior probabilities for genes
gene_prob_DOX_0.5 <- cormotif_DOX_0.5$bestmotif$p.post

rownames(gene_prob_DOX_0.5) <- rownames(Corrmotif_df)


prob_DOX_1_0.5  <- rownames(gene_prob_DOX_0.5[(gene_prob_DOX_0.5[,1] <0.5 & gene_prob_DOX_0.5[,2] <0.5 & gene_prob_DOX_0.5[,3]<0.5),])
length(prob_DOX_1_0.5)

[1] 7320

prob_DOX_2_0.5  <- rownames(gene_prob_DOX_0.5[(gene_prob_DOX_0.5[,1] >0.02 & gene_prob_DOX_0.5[,2] >0.5 & gene_prob_DOX_0.5[,3]>0.5),])
length(prob_DOX_2_0.5)

[1] 6890

📌 Save Corr-motif datasets for Gene Ontology analysis

write.csv(data.frame(Entrez_ID = prob_CX_1_0.1), "data/prob_CX_1_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_CX_2_0.1), "data/prob_CX_2_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_CX_1_0.5), "data/prob_CX_1_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_CX_2_0.5), "data/prob_CX_2_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_DOX_1_0.1), "data/prob_DOX_1_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_DOX_2_0.1), "data/prob_DOX_2_0.1.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_DOX_1_0.5), "data/prob_DOX_1_0.5.csv", row.names = FALSE)
write.csv(data.frame(Entrez_ID = prob_DOX_2_0.5), "data/prob_DOX_2_0.5.csv", row.names = FALSE)

📌 Absolute logFC Boxplot (CX response groups)

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_CX_1_0.1 <- as.character(read.csv("data/prob_CX_1_0.1.csv")$Entrez_ID)
prob_CX_2_0.1 <- as.character(read.csv("data/prob_CX_2_0.1.csv")$Entrez_ID)
prob_CX_1_0.5 <- as.character(read.csv("data/prob_CX_1_0.5.csv")$Entrez_ID)
prob_CX_2_0.5 <- as.character(read.csv("data/prob_CX_2_0.5.csv")$Entrez_ID)

# Load Datasets (CX 0.1 & CX 0.5 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")

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")

# Combine Data for CX 0.1 micromolar
CX_toptables_0.1 <- bind_rows(
  CX_0.1_3  %>% mutate(Timepoint = "3hr", Concentration = "CX_0.1 micromolar"),
  CX_0.1_24 %>% mutate(Timepoint = "24hr", Concentration = "CX_0.1 micromolar"),
  CX_0.1_48 %>% mutate(Timepoint = "48hr", Concentration = "CX_0.1 micromolar")
)

# Combine Data for CX 0.5 micromolar
CX_toptables_0.5 <- bind_rows(
  CX_0.5_3  %>% mutate(Timepoint = "3hr", Concentration = "CX_0.5 micromolar"),
  CX_0.5_24 %>% mutate(Timepoint = "24hr", Concentration = "CX_0.5 micromolar"),
  CX_0.5_48 %>% mutate(Timepoint = "48hr", Concentration = "CX_0.5 micromolar")
)

# Assign Response Groups for CX 0.1
CX_toptables_0.1 <- CX_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_CX_1_0.1 ~ "CX_low_non response",
      Entrez_ID %in% prob_CX_2_0.1 ~ "CX_low_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group)) %>%
  mutate(absFC = abs(logFC))

# Assign Response Groups for CX 0.5
CX_toptables_0.5 <- CX_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_CX_1_0.5 ~ "CX_high_non response",
      Entrez_ID %in% prob_CX_2_0.5 ~ "CX_high_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group)) %>%
  mutate(absFC = abs(logFC))

# Merge Both Concentrations Separately (Keeping them Separate)
CX_toptables_0.1$Category <- "CX_0.1 micromolar"
CX_toptables_0.5$Category <- "CX_0.5 micromolar"

CX_toptables <- bind_rows(CX_toptables_0.1, CX_toptables_0.5)

# Convert Factors for Proper Ordering
CX_toptables$Timepoint <- factor(CX_toptables$Timepoint, levels = c("3hr", "24hr", "48hr"))
CX_toptables$Response_Group <- factor(CX_toptables$Response_Group, levels = c(
  "CX_low_non response", "CX_low_mid_late_response",
  "CX_high_non response", "CX_high_mid_late_response"
))

# **Plot Faceted Boxplot (Each Response Group Separate)**
ggplot(CX_toptables, aes(x = Timepoint, y = absFC, fill = Category)) +
  geom_boxplot() +
  facet_wrap(~ Response_Group, ncol = 2) +  # Two columns, ensuring a 2x2 grid
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "|Log Fold Change|",
    title = "|Log Fold Change| boxplots across CX response groups"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")  # Formatting facet labels
  ) +
  scale_fill_manual(values = c("CX_0.1 micromolar" = "blue", "CX_0.5 micromolar" = "red"))

Past versions of "abs logFC-1.png"

Version	Author	Date
6121538	sayanpaul01	2025-03-03
fb09f5f	sayanpaul01	2025-03-03

📌 Absolute logFC Boxplot (DOX response groups)

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_DOX_1_0.1 <- as.character(read.csv("data/prob_DOX_1_0.1.csv")$Entrez_ID)
prob_DOX_2_0.1 <- as.character(read.csv("data/prob_DOX_2_0.1.csv")$Entrez_ID)
prob_DOX_1_0.5 <- as.character(read.csv("data/prob_DOX_1_0.5.csv")$Entrez_ID)
prob_DOX_2_0.5 <- as.character(read.csv("data/prob_DOX_2_0.5.csv")$Entrez_ID)

# Load Datasets (DOX 0.1 & DOX 0.5 Micromolar)
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")

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 Data for DOX 0.1 micromolar
DOX_toptables_0.1 <- bind_rows(
  DOX_0.1_3  %>% mutate(Timepoint = "3hr", Concentration = "DOX_0.1 micromolar"),
  DOX_0.1_24 %>% mutate(Timepoint = "24hr", Concentration = "DOX_0.1 micromolar"),
  DOX_0.1_48 %>% mutate(Timepoint = "48hr", Concentration = "DOX_0.1 micromolar")
)

# Combine Data for DOX 0.5 micromolar
DOX_toptables_0.5 <- bind_rows(
  DOX_0.5_3  %>% mutate(Timepoint = "3hr", Concentration = "DOX_0.5 micromolar"),
  DOX_0.5_24 %>% mutate(Timepoint = "24hr", Concentration = "DOX_0.5 micromolar"),
  DOX_0.5_48 %>% mutate(Timepoint = "48hr", Concentration = "DOX_0.5 micromolar")
)

# Assign Response Groups for DOX 0.1
DOX_toptables_0.1 <- DOX_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_DOX_1_0.1 ~ "DOX_low_non response",
      Entrez_ID %in% prob_DOX_2_0.1 ~ "DOX_low_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group)) %>%
  mutate(absFC = abs(logFC))

# Assign Response Groups for DOX 0.5
DOX_toptables_0.5 <- DOX_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_DOX_1_0.5 ~ "DOX_high_non response",
      Entrez_ID %in% prob_DOX_2_0.5 ~ "DOX_high_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group)) %>%
  mutate(absFC = abs(logFC))

# Merge Both Concentrations Separately (Keeping them Separate)
DOX_toptables_0.1$Category <- "DOX_0.1 micromolar"
DOX_toptables_0.5$Category <- "DOX_0.5 micromolar"

DOX_toptables <- bind_rows(DOX_toptables_0.1, DOX_toptables_0.5)

# Convert Factors for Proper Ordering
DOX_toptables$Timepoint <- factor(DOX_toptables$Timepoint, levels = c("3hr", "24hr", "48hr"))
DOX_toptables$Response_Group <- factor(DOX_toptables$Response_Group, levels = c(
  "DOX_low_non response", "DOX_low_mid_late_response",
  "DOX_high_non response", "DOX_high_mid_late_response"
))

# **Plot Faceted Boxplot (Each Response Group Separate)**
ggplot(DOX_toptables, aes(x = Timepoint, y = absFC, fill = Category)) +
  geom_boxplot() +
  facet_wrap(~ Response_Group, ncol = 2) +  # Two columns, ensuring a 2x2 grid
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "|Log Fold Change|",
    title = "|Log Fold Change| boxplots across DOX response groups"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")  # Formatting facet labels
  ) +
  scale_fill_manual(values = c("DOX_0.1 micromolar" = "blue", "DOX_0.5 micromolar" = "red"))

Past versions of "abs logFC DOX-1.png"

Version	Author	Date
fb09f5f	sayanpaul01	2025-03-03

📌 logFC Boxplot (CX response groups)

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_CX_1_0.1 <- as.character(read.csv("data/prob_CX_1_0.1.csv")$Entrez_ID)
prob_CX_2_0.1 <- as.character(read.csv("data/prob_CX_2_0.1.csv")$Entrez_ID)
prob_CX_1_0.5 <- as.character(read.csv("data/prob_CX_1_0.5.csv")$Entrez_ID)
prob_CX_2_0.5 <- as.character(read.csv("data/prob_CX_2_0.5.csv")$Entrez_ID)

# Load Datasets (CX 0.1 & CX 0.5 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")

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")

# Combine Data for CX 0.1 micromolar
CX_toptables_0.1 <- bind_rows(
  CX_0.1_3  %>% mutate(Timepoint = "3hr", Concentration = "CX_0.1 micromolar"),
  CX_0.1_24 %>% mutate(Timepoint = "24hr", Concentration = "CX_0.1 micromolar"),
  CX_0.1_48 %>% mutate(Timepoint = "48hr", Concentration = "CX_0.1 micromolar")
)

# Combine Data for CX 0.5 micromolar
CX_toptables_0.5 <- bind_rows(
  CX_0.5_3  %>% mutate(Timepoint = "3hr", Concentration = "CX_0.5 micromolar"),
  CX_0.5_24 %>% mutate(Timepoint = "24hr", Concentration = "CX_0.5 micromolar"),
  CX_0.5_48 %>% mutate(Timepoint = "48hr", Concentration = "CX_0.5 micromolar")
)

# Assign Response Groups for CX 0.1
CX_toptables_0.1 <- CX_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_CX_1_0.1 ~ "CX_low_non response",
      Entrez_ID %in% prob_CX_2_0.1 ~ "CX_low_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Assign Response Groups for CX 0.5
CX_toptables_0.5 <- CX_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_CX_1_0.5 ~ "CX_high_non response",
      Entrez_ID %in% prob_CX_2_0.5 ~ "CX_high_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Merge Both Concentrations Separately (Keeping them Separate)
CX_toptables_0.1$Category <- "CX_0.1 micromolar"
CX_toptables_0.5$Category <- "CX_0.5 micromolar"

CX_toptables <- bind_rows(CX_toptables_0.1, CX_toptables_0.5)

# Convert Factors for Proper Ordering
CX_toptables$Timepoint <- factor(CX_toptables$Timepoint, levels = c("3hr", "24hr", "48hr"))
CX_toptables$Response_Group <- factor(CX_toptables$Response_Group, levels = c(
  "CX_low_non response", "CX_low_mid_late_response",
  "CX_high_non response", "CX_high_mid_late_response"
))

# **Plot Faceted Boxplot (Using LogFC)**
ggplot(CX_toptables, aes(x = Timepoint, y = logFC, fill = Category)) +
  geom_boxplot() +
  facet_wrap(~ Response_Group, ncol = 2) +  # Two columns, ensuring a 2x2 grid
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Log Fold Change",
    title = "Log Fold Change Boxplots Across CX Response Groups"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")  # Formatting facet labels
  ) +
  scale_fill_manual(values = c("CX_0.1 micromolar" = "blue", "CX_0.5 micromolar" = "red"))

Past versions of "logFC CX-1.png"

Version	Author	Date
fb09f5f	sayanpaul01	2025-03-03

📌 logFC Boxplot (DOX response groups)

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_DOX_1_0.1 <- as.character(read.csv("data/prob_DOX_1_0.1.csv")$Entrez_ID)
prob_DOX_2_0.1 <- as.character(read.csv("data/prob_DOX_2_0.1.csv")$Entrez_ID)
prob_DOX_1_0.5 <- as.character(read.csv("data/prob_DOX_1_0.5.csv")$Entrez_ID)
prob_DOX_2_0.5 <- as.character(read.csv("data/prob_DOX_2_0.5.csv")$Entrez_ID)

# Load Datasets (DOX 0.1 & DOX 0.5 Micromolar)
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")

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 Data for DOX 0.1 micromolar
DOX_toptables_0.1 <- bind_rows(
  DOX_0.1_3  %>% mutate(Timepoint = "3hr", Concentration = "DOX_0.1 micromolar"),
  DOX_0.1_24 %>% mutate(Timepoint = "24hr", Concentration = "DOX_0.1 micromolar"),
  DOX_0.1_48 %>% mutate(Timepoint = "48hr", Concentration = "DOX_0.1 micromolar")
)

# Combine Data for DOX 0.5 micromolar
DOX_toptables_0.5 <- bind_rows(
  DOX_0.5_3  %>% mutate(Timepoint = "3hr", Concentration = "DOX_0.5 micromolar"),
  DOX_0.5_24 %>% mutate(Timepoint = "24hr", Concentration = "DOX_0.5 micromolar"),
  DOX_0.5_48 %>% mutate(Timepoint = "48hr", Concentration = "DOX_0.5 micromolar")
)

# Assign Response Groups for DOX 0.1
DOX_toptables_0.1 <- DOX_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_DOX_1_0.1 ~ "DOX_low_non response",
      Entrez_ID %in% prob_DOX_2_0.1 ~ "DOX_low_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Assign Response Groups for DOX 0.5
DOX_toptables_0.5 <- DOX_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_DOX_1_0.5 ~ "DOX_high_non response",
      Entrez_ID %in% prob_DOX_2_0.5 ~ "DOX_high_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Merge Both Concentrations Separately (Keeping them Separate)
DOX_toptables_0.1$Category <- "DOX_0.1 micromolar"
DOX_toptables_0.5$Category <- "DOX_0.5 micromolar"

DOX_toptables <- bind_rows(DOX_toptables_0.1, DOX_toptables_0.5)

# Convert Factors for Proper Ordering
DOX_toptables$Timepoint <- factor(DOX_toptables$Timepoint, levels = c("3hr", "24hr", "48hr"))
DOX_toptables$Response_Group <- factor(DOX_toptables$Response_Group, levels = c(
  "DOX_low_non response", "DOX_low_mid_late_response",
  "DOX_high_non response", "DOX_high_mid_late_response"
))

# **Plot Faceted Boxplot (Using LogFC)**
ggplot(DOX_toptables, aes(x = Timepoint, y = logFC, fill = Category)) +
  geom_boxplot() +
  facet_wrap(~ Response_Group, ncol = 2) +  # Two columns, ensuring a 2x2 grid
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Log Fold Change",
    title = "Log Fold Change Boxplots Across DOX Response Groups"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")  # Formatting facet labels
  ) +
  scale_fill_manual(values = c("DOX_0.1 micromolar" = "blue", "DOX_0.5 micromolar" = "red"))

Past versions of "logFC DOX-1.png"

Version	Author	Date
fb09f5f	sayanpaul01	2025-03-03

📌 Mean (Abs logFC) across timepoints (CX response groups)

# Load required libraries
# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_CX_1_0.1 <- as.character(read.csv("data/prob_CX_1_0.1.csv")$Entrez_ID)
prob_CX_2_0.1 <- as.character(read.csv("data/prob_CX_2_0.1.csv")$Entrez_ID)
prob_CX_1_0.5 <- as.character(read.csv("data/prob_CX_1_0.5.csv")$Entrez_ID)
prob_CX_2_0.5 <- as.character(read.csv("data/prob_CX_2_0.5.csv")$Entrez_ID)

# Load Datasets (CX 0.1 & CX 0.5 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")

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")

# Combine Data for CX 0.1 micromolar
CX_toptables_0.1 <- bind_rows(
  CX_0.1_3  %>% mutate(Timepoint = "3hr", Concentration = "CX_0.1 micromolar"),
  CX_0.1_24 %>% mutate(Timepoint = "24hr", Concentration = "CX_0.1 micromolar"),
  CX_0.1_48 %>% mutate(Timepoint = "48hr", Concentration = "CX_0.1 micromolar")
)

# Combine Data for CX 0.5 micromolar
CX_toptables_0.5 <- bind_rows(
  CX_0.5_3  %>% mutate(Timepoint = "3hr", Concentration = "CX_0.5 micromolar"),
  CX_0.5_24 %>% mutate(Timepoint = "24hr", Concentration = "CX_0.5 micromolar"),
  CX_0.5_48 %>% mutate(Timepoint = "48hr", Concentration = "CX_0.5 micromolar")
)

# Assign Response Groups for CX 0.1
CX_toptables_0.1 <- CX_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_CX_1_0.1 ~ "CX_low_non response",
      Entrez_ID %in% prob_CX_2_0.1 ~ "CX_low_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group)) %>%
  mutate(absFC = abs(logFC))

# Assign Response Groups for CX 0.5
CX_toptables_0.5 <- CX_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_CX_1_0.5 ~ "CX_high_non response",
      Entrez_ID %in% prob_CX_2_0.5 ~ "CX_high_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group)) %>%
  mutate(absFC = abs(logFC))

# Merge Both Concentrations Separately (Keeping them Separate)
CX_toptables_0.1$Category <- "CX_0.1 micromolar"
CX_toptables_0.5$Category <- "CX_0.5 micromolar"

CX_toptables <- bind_rows(CX_toptables_0.1, CX_toptables_0.5)

# Convert Factors for Proper Ordering
CX_toptables$Timepoint <- factor(CX_toptables$Timepoint, levels = c("3hr", "24hr", "48hr"))
CX_toptables$Response_Group <- factor(CX_toptables$Response_Group, levels = c(
  "CX_low_non response", "CX_low_mid_late_response",
  "CX_high_non response", "CX_high_mid_late_response"
))

# Calculate Mean Absolute logFC per Response Group, Timepoint, and Concentration
mean_absFC <- CX_toptables %>%
  group_by(Response_Group, Timepoint, Category) %>%
  summarise(mean_absFC = mean(absFC, na.rm = TRUE), .groups = "drop")

# **Plot Mean Absolute logFC Line Plot (Y-limit: 0 to 2)**
ggplot(mean_absFC, aes(x = Timepoint, y = mean_absFC, color = Category, group = Category)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  facet_wrap(~ Response_Group, ncol = 2) +  # Two columns, ensuring a 2x2 grid
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Mean |Log Fold Change|",
    title = "Mean |logFC| Across CX Response Groups"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")  # Formatting facet labels
  ) +
  scale_color_manual(values = c("CX_0.1 micromolar" = "blue", "CX_0.5 micromolar" = "red")) +
  ylim(0, 2)  # Set Y-axis limits from 0 to 2

Past versions of "Mean CX-1.png"

Version	Author	Date
fb09f5f	sayanpaul01	2025-03-03

📌 Mean (Abs logFC) across timepoints (DOX response groups)

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_DOX_1_0.1 <- as.character(read.csv("data/prob_DOX_1_0.1.csv")$Entrez_ID)
prob_DOX_2_0.1 <- as.character(read.csv("data/prob_DOX_2_0.1.csv")$Entrez_ID)
prob_DOX_1_0.5 <- as.character(read.csv("data/prob_DOX_1_0.5.csv")$Entrez_ID)
prob_DOX_2_0.5 <- as.character(read.csv("data/prob_DOX_2_0.5.csv")$Entrez_ID)

# Load Datasets (DOX 0.1 & DOX 0.5 Micromolar)
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")

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 Data for DOX 0.1 micromolar
DOX_toptables_0.1 <- bind_rows(
  DOX_0.1_3  %>% mutate(Timepoint = "3hr", Concentration = "DOX_0.1 micromolar"),
  DOX_0.1_24 %>% mutate(Timepoint = "24hr", Concentration = "DOX_0.1 micromolar"),
  DOX_0.1_48 %>% mutate(Timepoint = "48hr", Concentration = "DOX_0.1 micromolar")
)

# Combine Data for DOX 0.5 micromolar
DOX_toptables_0.5 <- bind_rows(
  DOX_0.5_3  %>% mutate(Timepoint = "3hr", Concentration = "DOX_0.5 micromolar"),
  DOX_0.5_24 %>% mutate(Timepoint = "24hr", Concentration = "DOX_0.5 micromolar"),
  DOX_0.5_48 %>% mutate(Timepoint = "48hr", Concentration = "DOX_0.5 micromolar")
)

# Assign Response Groups for DOX 0.1
DOX_toptables_0.1 <- DOX_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_DOX_1_0.1 ~ "DOX_low_non response",
      Entrez_ID %in% prob_DOX_2_0.1 ~ "DOX_low_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group)) %>%
  mutate(absFC = abs(logFC))

# Assign Response Groups for DOX 0.5
DOX_toptables_0.5 <- DOX_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_DOX_1_0.5 ~ "DOX_high_non response",
      Entrez_ID %in% prob_DOX_2_0.5 ~ "DOX_high_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group)) %>%
  mutate(absFC = abs(logFC))

# Merge Both Concentrations Separately (Keeping them Separate)
DOX_toptables_0.1$Category <- "DOX_0.1 micromolar"
DOX_toptables_0.5$Category <- "DOX_0.5 micromolar"

DOX_toptables <- bind_rows(DOX_toptables_0.1, DOX_toptables_0.5)

# Convert Factors for Proper Ordering
DOX_toptables$Timepoint <- factor(DOX_toptables$Timepoint, levels = c("3hr", "24hr", "48hr"))
DOX_toptables$Response_Group <- factor(DOX_toptables$Response_Group, levels = c(
  "DOX_low_non response", "DOX_low_mid_late_response",
  "DOX_high_non response", "DOX_high_mid_late_response"
))

# Calculate Mean Absolute logFC per Response Group, Timepoint, and Concentration
mean_absFC <- DOX_toptables %>%
  group_by(Response_Group, Timepoint, Category) %>%
  summarise(mean_absFC = mean(absFC, na.rm = TRUE), .groups = "drop")

# **Plot Mean Absolute logFC Line Plot (Y-limit: 0 to 2)**
ggplot(mean_absFC, aes(x = Timepoint, y = mean_absFC, color = Category, group = Category)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  facet_wrap(~ Response_Group, ncol = 2) +  # Two columns, ensuring a 2x2 grid
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Mean |Log Fold Change|",
    title = "Mean |logFC| Across DOX Response Groups"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")  # Formatting facet labels
  ) +
  scale_color_manual(values = c("DOX_0.1 micromolar" = "blue", "DOX_0.5 micromolar" = "red")) +
  ylim(0, 2)  # Set Y-axis limits from 0 to 2

Past versions of "Mean DOX-1.png"

Version	Author	Date
fb09f5f	sayanpaul01	2025-03-03

📌 Mean logFC across timepoints (CX response groups)

# Load required libraries
# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_CX_1_0.1 <- as.character(read.csv("data/prob_CX_1_0.1.csv")$Entrez_ID)
prob_CX_2_0.1 <- as.character(read.csv("data/prob_CX_2_0.1.csv")$Entrez_ID)
prob_CX_1_0.5 <- as.character(read.csv("data/prob_CX_1_0.5.csv")$Entrez_ID)
prob_CX_2_0.5 <- as.character(read.csv("data/prob_CX_2_0.5.csv")$Entrez_ID)

# Load Datasets (CX 0.1 & CX 0.5 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")

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")

# Combine Data for CX 0.1 micromolar
CX_toptables_0.1 <- bind_rows(
  CX_0.1_3  %>% mutate(Timepoint = "3hr", Concentration = "CX_0.1 micromolar"),
  CX_0.1_24 %>% mutate(Timepoint = "24hr", Concentration = "CX_0.1 micromolar"),
  CX_0.1_48 %>% mutate(Timepoint = "48hr", Concentration = "CX_0.1 micromolar")
)

# Combine Data for CX 0.5 micromolar
CX_toptables_0.5 <- bind_rows(
  CX_0.5_3  %>% mutate(Timepoint = "3hr", Concentration = "CX_0.5 micromolar"),
  CX_0.5_24 %>% mutate(Timepoint = "24hr", Concentration = "CX_0.5 micromolar"),
  CX_0.5_48 %>% mutate(Timepoint = "48hr", Concentration = "CX_0.5 micromolar")
)

# Assign Response Groups for CX 0.1
CX_toptables_0.1 <- CX_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_CX_1_0.1 ~ "CX_low_non response",
      Entrez_ID %in% prob_CX_2_0.1 ~ "CX_low_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Assign Response Groups for CX 0.5
CX_toptables_0.5 <- CX_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_CX_1_0.5 ~ "CX_high_non response",
      Entrez_ID %in% prob_CX_2_0.5 ~ "CX_high_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Merge Both Concentrations Separately (Keeping them Separate)
CX_toptables_0.1$Category <- "CX_0.1 micromolar"
CX_toptables_0.5$Category <- "CX_0.5 micromolar"

CX_toptables <- bind_rows(CX_toptables_0.1, CX_toptables_0.5)

# Convert Factors for Proper Ordering
CX_toptables$Timepoint <- factor(CX_toptables$Timepoint, levels = c("3hr", "24hr", "48hr"))
CX_toptables$Response_Group <- factor(CX_toptables$Response_Group, levels = c(
  "CX_low_non response", "CX_low_mid_late_response",
  "CX_high_non response", "CX_high_mid_late_response"
))

# Calculate Mean LogFC per Response Group, Timepoint, and Concentration
mean_logFC <- CX_toptables %>%
  group_by(Response_Group, Timepoint, Category) %>%
  summarise(mean_logFC = mean(logFC, na.rm = TRUE), .groups = "drop")

# **Plot Mean LogFC Line Plot (Y-limit: -2 to 2)**
ggplot(mean_logFC, aes(x = Timepoint, y = mean_logFC, color = Category, group = Category)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  facet_wrap(~ Response_Group, ncol = 2) +  # Two columns, ensuring a 2x2 grid
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Mean logFC",
    title = "Mean logFC across CX response groups"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")  # Formatting facet labels
  ) +
  scale_color_manual(values = c("CX_0.1 micromolar" = "blue", "CX_0.5 micromolar" = "red")) +
  ylim(-2, 2)  # Set Y-axis limits from -2 to 2

Past versions of "Mean CX2-1.png"

Version	Author	Date
fb09f5f	sayanpaul01	2025-03-03

📌 Mean logFC across timepoints (DOX response groups)

# Load required libraries
library(dplyr)
library(ggplot2)

# Load Response Groups from CSV Files
prob_DOX_1_0.1 <- as.character(read.csv("data/prob_DOX_1_0.1.csv")$Entrez_ID)
prob_DOX_2_0.1 <- as.character(read.csv("data/prob_DOX_2_0.1.csv")$Entrez_ID)
prob_DOX_1_0.5 <- as.character(read.csv("data/prob_DOX_1_0.5.csv")$Entrez_ID)
prob_DOX_2_0.5 <- as.character(read.csv("data/prob_DOX_2_0.5.csv")$Entrez_ID)

# Load Datasets (DOX 0.1 & DOX 0.5 Micromolar)
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")

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 Data for DOX 0.1 micromolar
DOX_toptables_0.1 <- bind_rows(
  DOX_0.1_3  %>% mutate(Timepoint = "3hr", Concentration = "DOX_0.1 micromolar"),
  DOX_0.1_24 %>% mutate(Timepoint = "24hr", Concentration = "DOX_0.1 micromolar"),
  DOX_0.1_48 %>% mutate(Timepoint = "48hr", Concentration = "DOX_0.1 micromolar")
)

# Combine Data for DOX 0.5 micromolar
DOX_toptables_0.5 <- bind_rows(
  DOX_0.5_3  %>% mutate(Timepoint = "3hr", Concentration = "DOX_0.5 micromolar"),
  DOX_0.5_24 %>% mutate(Timepoint = "24hr", Concentration = "DOX_0.5 micromolar"),
  DOX_0.5_48 %>% mutate(Timepoint = "48hr", Concentration = "DOX_0.5 micromolar")
)

# Assign Response Groups for DOX 0.1
DOX_toptables_0.1 <- DOX_toptables_0.1 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_DOX_1_0.1 ~ "DOX_low_non response",
      Entrez_ID %in% prob_DOX_2_0.1 ~ "DOX_low_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Assign Response Groups for DOX 0.5
DOX_toptables_0.5 <- DOX_toptables_0.5 %>%
  mutate(
    Response_Group = case_when(
      Entrez_ID %in% prob_DOX_1_0.5 ~ "DOX_high_non response",
      Entrez_ID %in% prob_DOX_2_0.5 ~ "DOX_high_mid_late_response",
      TRUE ~ NA_character_
    )
  ) %>%
  filter(!is.na(Response_Group))

# Merge Both Concentrations (Keeping them Separate)
DOX_toptables_0.1$Category <- "DOX_0.1 micromolar"
DOX_toptables_0.5$Category <- "DOX_0.5 micromolar"

DOX_toptables <- bind_rows(DOX_toptables_0.1, DOX_toptables_0.5)

# Convert Factors for Proper Ordering
DOX_toptables$Timepoint <- factor(DOX_toptables$Timepoint, levels = c("3hr", "24hr", "48hr"))
DOX_toptables$Response_Group <- factor(DOX_toptables$Response_Group, levels = c(
  "DOX_low_non response", "DOX_low_mid_late_response",
  "DOX_high_non response", "DOX_high_mid_late_response"
))

# Calculate Mean LogFC per Response Group, Timepoint, and Concentration
mean_logFC <- DOX_toptables %>%
  group_by(Response_Group, Timepoint, Category) %>%
  summarise(mean_logFC = mean(logFC, na.rm = TRUE), .groups = "drop")

# **Plot Mean LogFC Line Plot (Y-limit: -2 to 2)**
ggplot(mean_logFC, aes(x = Timepoint, y = mean_logFC, color = Category, group = Category)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  facet_wrap(~ Response_Group, ncol = 2) +  # Two columns, ensuring a 2x2 grid
  theme_bw() +
  labs(
    x = "Timepoints",
    y = "Mean logFC",
    title = "Mean logFC across DOX response groups"
  ) +
  theme(
    plot.title = element_text(size = rel(1.5), hjust = 0.5),
    strip.text = element_text(size = 12, face = "bold")  # Formatting facet labels
  ) +
  scale_color_manual(values = c("DOX_0.1 micromolar" = "blue", "DOX_0.5 micromolar" = "red")) +
  ylim(-2, 2)  # Set Y-axis limits from -2 to 2

Past versions of "Mean DOX2-1.png"

Version	Author	Date
fb09f5f	sayanpaul01	2025-03-03

Session information

sessionInfo()

R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggplot2_3.5.1       gprofiler2_0.2.3    BiocParallel_1.36.0
 [4] dplyr_1.1.4         Rfast_2.1.0         RcppParallel_5.1.9 
 [7] RcppZiggurat_0.1.6  Rcpp_1.0.12         Cormotif_1.48.0    
[10] limma_3.58.1        affy_1.80.0         Biobase_2.62.0     
[13] BiocGenerics_0.48.1 workflowr_1.7.1    

loaded via a namespace (and not attached):
 [1] gtable_0.3.6          xfun_0.50             bslib_0.8.0          
 [4] htmlwidgets_1.6.4     processx_3.8.5        callr_3.7.6          
 [7] vctrs_0.6.5           tools_4.3.0           ps_1.8.1             
[10] generics_0.1.3        parallel_4.3.0        tibble_3.2.1         
[13] pkgconfig_2.0.3       data.table_1.14.10    lifecycle_1.0.4      
[16] farver_2.1.2          compiler_4.3.0        stringr_1.5.1        
[19] git2r_0.35.0          statmod_1.5.0         munsell_0.5.1        
[22] getPass_0.2-4         codetools_0.2-20      httpuv_1.6.15        
[25] htmltools_0.5.8.1     sass_0.4.9            yaml_2.3.10          
[28] lazyeval_0.2.2        preprocessCore_1.64.0 plotly_4.10.4        
[31] tidyr_1.3.1           later_1.3.2           pillar_1.10.1        
[34] jquerylib_0.1.4       whisker_0.4.1         cachem_1.0.8         
[37] tidyselect_1.2.1      digest_0.6.34         stringi_1.8.3        
[40] purrr_1.0.2           labeling_0.4.3        rprojroot_2.0.4      
[43] fastmap_1.1.1         grid_4.3.0            colorspace_2.1-0     
[46] cli_3.6.1             magrittr_2.0.3        withr_3.0.2          
[49] scales_1.3.0          promises_1.3.0        rmarkdown_2.29       
[52] httr_1.4.7            affyio_1.72.0         evaluate_1.0.3       
[55] knitr_1.49            viridisLite_0.4.2     rlang_1.1.3          
[58] glue_1.7.0            BiocManager_1.30.25   rstudioapi_0.17.1    
[61] jsonlite_1.8.9        R6_2.5.1              fs_1.6.3             
[64] zlibbioc_1.48.0