PCA Analysis
Your Name
2025-05-23
Last updated: 2025-05-23
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Knit directory: CX5461_Project/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | ef3bb6c | sayanpaul01 | 2025-05-22 | Commit |
| html | ef3bb6c | sayanpaul01 | 2025-05-22 | Commit |
| html | dce4456 | sayanpaul01 | 2025-05-22 | Commit |
| Rmd | edfc7e1 | sayanpaul01 | 2025-05-22 | Commit |
| html | edfc7e1 | sayanpaul01 | 2025-05-22 | Commit |
| html | ffaf948 | sayanpaul01 | 2025-04-06 | Commit |
| Rmd | 0e53214 | sayanpaul01 | 2025-04-02 | Commit |
| html | 0e53214 | sayanpaul01 | 2025-04-02 | Commit |
| html | 3f3d8c0 | sayanpaul01 | 2025-02-02 | Build site. |
| Rmd | 56e44e6 | sayanpaul01 | 2025-02-02 | Fixed duplicate chunk labels in PCA analysis |
| html | 773671b | sayanpaul01 | 2025-02-01 | Build site. |
| Rmd | 91e6c2c | sayanpaul01 | 2025-02-01 | Fixed duplicate row names issue in count matrix |
Load Required Libraries
library(edgeR)Warning: package 'edgeR' was built under R version 4.3.2Warning: package 'limma' was built under R version 4.3.1library(ggplot2)
library(reshape2)
library(dplyr)Warning: package 'dplyr' was built under R version 4.3.2library(Biobase)Warning: package 'Biobase' was built under R version 4.3.1Warning: package 'BiocGenerics' was built under R version 4.3.1library(limma)
library(tidyverse)Warning: package 'tidyverse' was built under R version 4.3.2Warning: package 'tidyr' was built under R version 4.3.3Warning: 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(scales)Warning: package 'scales' was built under R version 4.3.2library(biomaRt)Warning: package 'biomaRt' was built under R version 4.3.2library(ggrepel)Warning: package 'ggrepel' was built under R version 4.3.3library(corrplot)Warning: package 'corrplot' was built under R version 4.3.3library(Hmisc)Warning: package 'Hmisc' 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 4.3.2library(AnnotationDbi)
library(tidyr)
library(ggfortify)📍 Load the Count Matrix CSV file
📌Color palettes
### 📌 Color palettes (updated)
drug_conc_palette <- c(
"CX-5461_0.1" = "gold", # light green
"CX-5461_0.5" = "green4", # dark green
"DOX_0.1" = "salmon2", # peach
"DOX_0.5" = "red3", # burnt orange
"VEH_0.1" = "lightblue3", # sky blue
"VEH_0.5" = "darkblue" # navy blue
)
drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
drug_palc1 <- c("#8B006D","#F1B72B", "#3386DD","#707031")
drug_palc2 <- c("#8B006D","#F1B72B", "#3386DD")📌Load Metadata
📌PCA of Unfiltered log2(CPM)
prcomp_res <- prcomp(t(matrix), center = TRUE)
ggplot2::autoplot(prcomp_res, data = Metadata,
colour = "Drug_Conc", shape = "Time", size = 4, x = 1, y = 2) +
ggrepel::geom_text_repel(label = Indiv) +
scale_color_manual(values = drug_conc_palette) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered")) +
theme_bw()Warning: ggrepel: 67 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res <- prcomp(t(lcpm %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res, data = Metadata, colour = "Condition", shape = "Time", size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered")) +
theme_bw()Warning: ggrepel: 33 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res <- prcomp(t(lcpm %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res, data = Metadata, colour = "Condition", shape = "Time", size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered")) +
theme_bw()Warning: ggrepel: 38 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
📌PCA of Filtered log2(CPM) (RowMeans > 0)
prcomp_res1 <- prcomp(t(filcpm_matrix %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res1, data = Metadata, colour = "Drug_Conc", shape = "Time", size =4, x=1, y=2) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_conc_palette) +
ggtitle(expression("PCA of gene expression (log2 cpm)")) +
theme_bw()Warning: ggrepel: 51 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res1 <- prcomp(t(filcpm_matrix %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res1, data = Metadata, colour = "Drug_Conc", shape = "Time", size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_conc_palette) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0)")) +
theme_bw()Warning: ggrepel: 22 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res1 <- prcomp(t(filcpm_matrix %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res1, data = Metadata, colour = "Condition", shape = "Time", size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0)")) +
theme_bw()Warning: ggrepel: 26 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
📌PC1–PC3 Gene Expression Variance Across Individual, Drug, Concentration, and Timepoint log2(CPM) (RowMeans > 0)
# 📌 Load Required Libraries
library(edgeR)
library(ggplot2)
library(dplyr)
library(tidyr)
library(ggrepel)
library(patchwork)Warning: package 'patchwork' was built under R version 4.3.3# 📌 Load and Filter Count Matrix
counts_matrix <- read.csv("data/counts_matrix.csv", header = TRUE, check.names = FALSE)
cpm <- cpm(counts_matrix)
lcpm <- cpm(counts_matrix, log = TRUE)
filcpm_matrix <- subset(lcpm, rowMeans(lcpm) > 0)
matrix <- as.matrix(filcpm_matrix)
# 📌 Load and Clean Metadata
Metadata <- read.csv("data/Metadata.csv")
Metadata$Time <- factor(Metadata$Time, levels = c(3, 24, 48), labels = c("3hr", "24hr", "48hr"))
Metadata$Ind <- factor(Metadata$Ind, levels = 1:6, labels = as.character(1:6))
Metadata$Drug <- as.character(Metadata$Drug)
Metadata$`Conc.` <- factor(Metadata$`Conc.`, levels = c(0.1, 0.5))
Metadata$Sex <- factor(Metadata$Sex, levels = c("Male", "Female")) # ✅ NEW: Sex factor
# 📌 PCA
prcomp_res <- prcomp(t(matrix), center = TRUE)
pca_df <- as.data.frame(prcomp_res$x[, 1:3]) # PC1–PC3
pca_df$Ind <- Metadata$Ind
pca_df$Drug <- Metadata$Drug
pca_df$Conc <- Metadata$`Conc.`
pca_df$Time <- Metadata$Time
pca_df$Sex <- Metadata$Sex # ✅ NEW: Add Sex to PCA dataframe
# 📌 p-value from linear model
get_regr_pval <- function(mod) {
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$fstatistic
pval <- 1 - pf(fstat[1], fstat[2], fstat[3])
return(pval)
}
# 📌 Boxplot function
plot_pc_box <- function(df, group_var, pc) {
group_data <- df[[group_var]]
n_groups <- length(unique(group_data))
if (n_groups > 1) {
model <- lm(df[[pc]] ~ group_data)
pval <- get_regr_pval(model)
pval_label <- paste0("p-value: ", signif(pval, 3))
} else {
pval_label <- "p-value: NA"
}
ggplot(df, aes(x = .data[[group_var]], y = .data[[pc]], fill = .data[[group_var]])) +
geom_boxplot(color = "black") +
theme_bw(base_size = 11) +
ylab(pc) + xlab(group_var) +
ggtitle(NULL, subtitle = pval_label) +
theme(
legend.position = "none",
plot.subtitle = element_text(size = 10),
panel.border = element_rect(color = "black", fill = NA)
)
}
# 📌 Generate 15 plots: PC1–3 × Ind, Drug, Conc, Time, Sex
pcs <- c("PC1", "PC2", "PC3")
group_vars <- c("Ind", "Drug", "Conc", "Time", "Sex") # ✅ Add "Sex"
plots <- list()
for (pc in pcs) {
for (group in group_vars) {
key <- paste(pc, group, sep = "_")
base_plot <- plot_pc_box(pca_df, group, pc)
if (pc == "PC1") {
upper_limit <- max(pca_df[[pc]], na.rm = TRUE) * 1.1
plots[[key]] <- base_plot +
scale_y_continuous(limits = c(-60, upper_limit),
breaks = c(-60, -30, 0, 30, 60, 90, 120))
} else {
plots[[key]] <- base_plot
}
}
}
# 📌 Remove main titles (retain subtitles for p-values)
plots <- lapply(plots, function(p) {
p + theme(plot.title = element_blank())
})
# 📌 Create column headers
header_ind <- ggplot() + theme_void() + ggtitle("Ind") + theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
header_drug <- ggplot() + theme_void() + ggtitle("Drug") + theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
header_conc <- ggplot() + theme_void() + ggtitle("Conc") + theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
header_time <- ggplot() + theme_void() + ggtitle("Time") + theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold"))
header_sex <- ggplot() + theme_void() + ggtitle("Sex") + theme(plot.title = element_text(hjust = 0.5, size = 14, face = "bold")) # ✅ New header
# 📌 Assemble 5-column layout with 3 PC rows
final_plot <- (
(header_ind | header_drug | header_conc | header_time | header_sex) /
(plots[["PC1_Ind"]] | plots[["PC1_Drug"]] | plots[["PC1_Conc"]] | plots[["PC1_Time"]] | plots[["PC1_Sex"]]) /
(plots[["PC2_Ind"]] | plots[["PC2_Drug"]] | plots[["PC2_Conc"]] | plots[["PC2_Time"]] | plots[["PC2_Sex"]]) /
(plots[["PC3_Ind"]] | plots[["PC3_Drug"]] | plots[["PC3_Conc"]] | plots[["PC3_Time"]] | plots[["PC3_Sex"]])
) + plot_layout(heights = c(0.07, 1, 1, 1)) # Title row height
# 📌 Display the plot
print(final_plot)
| Version | Author | Date |
|---|---|---|
| ef3bb6c | sayanpaul01 | 2025-05-22 |
📌PCA of Filtered log2(CPM) (RowMeans > 0.5)
prcomp_res2 <- prcomp(t(filcpm_matrix1 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res2, data = Metadata, colour = "Condition", shape = "Time", size =4, x=1, y=2) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0.5)")) +
theme_bw()Warning: ggrepel: 54 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res2 <- prcomp(t(filcpm_matrix1 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res2, data = Metadata, colour = "Condition", shape = "Time", size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0.5)")) +
theme_bw()Warning: ggrepel: 28 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res2 <- prcomp(t(filcpm_matrix1 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res2, data = Metadata, colour = "Condition", shape = "Time", size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >0.5)")) +
theme_bw()Warning: ggrepel: 26 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
📌PCA of Filtered log2(CPM) (RowMeans > 1)
prcomp_res3 <- prcomp(t(filcpm_matrix2 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res3, data = Metadata, colour = "Condition", shape = "Time", size =4, x=1, y=2) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >1)")) +
theme_bw()Warning: ggrepel: 60 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res3 <- prcomp(t(filcpm_matrix2 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res3, data = Metadata, colour = "Condition", shape = "Time", size =4, x=2, y=3) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >1)")) +
theme_bw()Warning: ggrepel: 31 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res3 <- prcomp(t(filcpm_matrix2 %>% as.matrix()), center = TRUE)
ggplot2::autoplot(prcomp_res3, data = Metadata, colour = "Condition", shape = "Time", size =4, x=3, y=4) +
ggrepel::geom_text_repel(label=Indiv) +
scale_color_manual(values=drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) filtered (Rowmeans >1)")) +
theme_bw()Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
📌 PCA Analysis by Drugs
📌 PCA Analysis: CX-5461 & VEH
selected_columns <- grepl("VEH|CX.5461", colnames(matrix))
subset_matrix_CX <- matrix[, selected_columns]
subset_meta <- subset(Metadata, Metadata$Drug %in% c("VEH", "CX-5461"))
prcomp_res4 <- prcomp(t(subset_matrix_CX), center = TRUE)
ggplot2::autoplot(prcomp_res4, data = as.data.frame(subset_meta), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta$Ind) + # ✅ Corrected label
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered (CX-5461 vs VEH)")) +
theme_bw()Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res5 <- prcomp(t(subset_matrix_CX[rowMeans(subset_matrix_CX) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res5, data = as.data.frame(subset_meta), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (CX-5461 vs VEH)")) +
theme_bw()Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res6 <- prcomp(t(subset_matrix_CX[rowMeans(subset_matrix_CX) > 0.5, ]), center = TRUE)
ggplot2::autoplot(prcomp_res6, data = as.data.frame(subset_meta), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0.5 (CX-5461 vs VEH)")) +
theme_bw()Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res7 <- prcomp(t(subset_matrix_CX[rowMeans(subset_matrix_CX) > 1, ]), center = TRUE)
ggplot2::autoplot(prcomp_res7, data = as.data.frame(subset_meta), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 1 (CX-5461 vs VEH)")) +
theme_bw()
📌 PCA Analysis: DOX & VEH
selected_columns <- grepl("VEH|DOX", colnames(matrix))
subset_matrix_DOX <- matrix[, selected_columns]
subset_meta_dox <- subset(Metadata, Metadata$Drug %in% c("VEH", "DOX"))
prcomp_res8 <- prcomp(t(subset_matrix_DOX), center = TRUE)
ggplot2::autoplot(prcomp_res8, data = as.data.frame(subset_meta_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered (DOX vs VEH)")) +
theme_bw()Warning: ggrepel: 31 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res9 <- prcomp(t(subset_matrix_DOX[rowMeans(subset_matrix_DOX) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res9, data = as.data.frame(subset_meta_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (DOX vs VEH)")) +
theme_bw()Warning: ggrepel: 31 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res10 <- prcomp(t(subset_matrix_DOX[rowMeans(subset_matrix_DOX) > 0.5, ]), center = TRUE)
ggplot2::autoplot(prcomp_res10, data = as.data.frame(subset_meta_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0.5 (DOX vs VEH)")) +
theme_bw()Warning: ggrepel: 30 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res11 <- prcomp(t(subset_matrix_DOX[rowMeans(subset_matrix_DOX) > 1, ]), center = TRUE)
ggplot2::autoplot(prcomp_res11, data = as.data.frame(subset_meta_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 1 (DOX vs VEH)")) +
theme_bw()Warning: ggrepel: 34 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
📌 PCA Analysis: CX-5461 & DOX
selected_columns <- grepl("CX.5461|DOX", colnames(matrix))
subset_matrix_CX_DOX <- matrix[, selected_columns]
subset_meta_cx_dox <- subset(Metadata, Metadata$Drug %in% c("CX-5461", "DOX"))
prcomp_res12 <- prcomp(t(subset_matrix_CX_DOX), center = TRUE)
ggplot2::autoplot(prcomp_res12, data = as.data.frame(subset_meta_cx_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_cx_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Unfiltered (CX-5461 vs DOX)")) +
theme_bw()Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res13 <- prcomp(t(subset_matrix_CX_DOX[rowMeans(subset_matrix_CX_DOX) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res13, data = as.data.frame(subset_meta_cx_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_cx_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (CX-5461 vs DOX)")) +
theme_bw()Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res14 <- prcomp(t(subset_matrix_CX_DOX[rowMeans(subset_matrix_CX_DOX) > 0.5, ]), center = TRUE)
ggplot2::autoplot(prcomp_res14, data = as.data.frame(subset_meta_cx_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_cx_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0.5 (CX-5461 vs DOX)")) +
theme_bw()Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res15 <- prcomp(t(subset_matrix_CX_DOX[rowMeans(subset_matrix_CX_DOX) > 1, ]), center = TRUE)
ggplot2::autoplot(prcomp_res15, data = as.data.frame(subset_meta_cx_dox), colour = "Condition", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_cx_dox$Ind) +
scale_color_manual(values = drug_palc1) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 1 (CX-5461 vs DOX)")) +
theme_bw()Warning: ggrepel: 21 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
📌 PCA Analysis by Timepoints
📌 3-Hour Timepoint
# 🎯 3-Hour PCA Subset
Metadata$Drug_Conc <- paste(Metadata$Drug, Metadata$Conc, sep = "_")
selected_columns <- grepl("_3", colnames(matrix))
subset_matrix_3hr <- matrix[, selected_columns]
subset_meta_3hr <- subset(Metadata, Metadata$Time == "3hr")
# Filter genes by rowMeans > 0 for 3hr samples
filtered_matrix_3hr <- subset_matrix_3hr[rowMeans(subset_matrix_3hr) > 0, ]
if (nrow(filtered_matrix_3hr) > 2) {
# Run PCA
prcomp_res_3hr <- prcomp(t(filtered_matrix_3hr), center = TRUE)
# Combine PCA coordinates with metadata
pca_3hr_df <- as.data.frame(prcomp_res_3hr$x[, 1:2]) # PC1 and PC2
pca_3hr_df$Ind <- subset_meta_3hr$Ind
pca_3hr_df$Drug <- subset_meta_3hr$Drug
pca_3hr_df$Drug_Conc <- subset_meta_3hr$Drug_Conc
# Plot
ggplot(pca_3hr_df, aes(x = PC1, y = PC2, colour = Drug_Conc, shape = Drug)) +
geom_point(size = 4) +
ggrepel::geom_text_repel(aes(label = Ind)) +
scale_color_manual(values = drug_conc_palette) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (3 Hours)")) +
theme_bw()
} else {
print("No genes passed the rowMeans > 0 filter for 3hr samples.")
}
prcomp_res_3hr <- prcomp(t(subset_matrix_3hr[rowMeans(subset_matrix_3hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_3hr, data = as.data.frame(subset_meta_3hr), colour = "Condition", shape = "Drug", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_3hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (3 Hours)")) +
theme_bw()
prcomp_res_3hr <- prcomp(t(subset_matrix_3hr[rowMeans(subset_matrix_3hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_3hr, data = as.data.frame(subset_meta_3hr), colour = "Condition", shape = "Drug", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_3hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (3 Hours)")) +
theme_bw()
📌 24-Hour Timepoint
# 📌 Subset for 24hr samples
selected_columns <- grepl("_24", colnames(matrix))
subset_matrix_24hr <- matrix[, selected_columns]
subset_meta_24hr <- subset(Metadata, Metadata$Time == "24hr") # match your relabeled timepoints
# 📌 Filter low-expression genes
filtered_matrix_24hr <- subset_matrix_24hr[rowMeans(subset_matrix_24hr) > 0, ]
# 📌 Run PCA if genes remain
if (nrow(filtered_matrix_24hr) > 2) {
prcomp_res_24hr <- prcomp(t(filtered_matrix_24hr), center = TRUE)
ggplot2::autoplot(prcomp_res_24hr, data = as.data.frame(subset_meta_24hr),
colour = "Drug_Conc", shape = "Drug", size = 4) +
ggrepel::geom_text_repel(aes(label = Ind)) +
scale_color_manual(values = drug_conc_palette) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (24 Hours)")) +
theme_bw()
} else {
message("⚠️ No genes passed the rowMeans > 0 filter for 24-hour samples.")
}Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res_24hr <- prcomp(t(subset_matrix_24hr[rowMeans(subset_matrix_24hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_24hr, data = as.data.frame(subset_meta_24hr), colour = "Condition", shape = "Drug", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_24hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (24 Hours)")) +
theme_bw()
prcomp_res_24hr <- prcomp(t(subset_matrix_24hr[rowMeans(subset_matrix_24hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_24hr, data = as.data.frame(subset_meta_24hr), colour = "Condition", shape = "Drug", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_24hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (24 Hours)")) +
theme_bw()
📌 48-Hour Timepoint
# 📌 Subset for 48hr samples
selected_columns <- grepl("_48", colnames(matrix))
subset_matrix_48hr <- matrix[, selected_columns]
subset_meta_48hr <- subset(Metadata, Metadata$Time == "48hr") # must match relabeled levels
# 📌 Filter low-expression genes
filtered_matrix_48hr <- subset_matrix_48hr[rowMeans(subset_matrix_48hr) > 0, ]
# 📌 Run PCA only if data is valid
if (nrow(filtered_matrix_48hr) > 2) {
prcomp_res_48hr_1 <- prcomp(t(filtered_matrix_48hr), center = TRUE)
ggplot2::autoplot(prcomp_res_48hr_1, data = as.data.frame(subset_meta_48hr),
colour = "Drug_Conc", shape = "Drug", size = 4) +
ggrepel::geom_text_repel(aes(label = Ind)) +
scale_color_manual(values = drug_conc_palette) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (48 Hours)")) +
theme_bw()
} else {
message("⚠️ No genes passed the rowMeans > 0 filter for 48-hour samples.")
}Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res_48hr_1 <- prcomp(t(subset_matrix_48hr[rowMeans(subset_matrix_48hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_48hr_1, data = as.data.frame(subset_meta_48hr), colour = "Condition", shape = "Drug", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_48hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (48 Hours)")) +
theme_bw()
prcomp_res_48hr_1 <- prcomp(t(subset_matrix_48hr[rowMeans(subset_matrix_48hr) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_48hr_1, data = as.data.frame(subset_meta_48hr), colour = "Condition", shape = "Drug", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_48hr$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (48 Hours)")) +
theme_bw()
📌 PCA Analysis by Concentrations
📌0.1 µM Concentration
selected_columns <- grepl("_0.1_", colnames(matrix))
subset_matrix_0.1 <- matrix[, selected_columns]
subset_meta_0.1 <- subset(Metadata, Metadata$Conc. == 0.1)
prcomp_res_0.1 <- prcomp(t(subset_matrix_0.1[rowMeans(subset_matrix_0.1) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.1, data = as.data.frame(subset_meta_0.1), colour = "Drug", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_0.1$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.1 µM)")) +
theme_bw()
prcomp_res_0.1 <- prcomp(t(subset_matrix_0.1[rowMeans(subset_matrix_0.1) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.1, data = as.data.frame(subset_meta_0.1), colour = "Drug", shape = "Time", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_0.1$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.1 µM)")) +
theme_bw()
prcomp_res_0.1 <- prcomp(t(subset_matrix_0.1[rowMeans(subset_matrix_0.1) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.1, data = as.data.frame(subset_meta_0.1), colour = "Drug", shape = "Time", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_0.1$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.1 µM)")) +
theme_bw()
📌0.5 µM Concentration
selected_columns <- grepl("_0.5_", colnames(matrix))
subset_matrix_0.5 <- matrix[, selected_columns]
subset_meta_0.5 <- subset(Metadata, Metadata$Conc. == 0.5)
prcomp_res_0.5 <- prcomp(t(subset_matrix_0.5[rowMeans(subset_matrix_0.5) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.5, data = as.data.frame(subset_meta_0.5), colour = "Drug", shape = "Time", size = 4) +
ggrepel::geom_text_repel(label = subset_meta_0.5$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.5 µM)")) +
theme_bw()Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
prcomp_res_0.5 <- prcomp(t(subset_matrix_0.5[rowMeans(subset_matrix_0.5) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.5, data = as.data.frame(subset_meta_0.5), colour = "Drug", shape = "Time", size = 4, x=2, y=3) +
ggrepel::geom_text_repel(label = subset_meta_0.5$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.5 µM)")) +
theme_bw()
prcomp_res_0.5 <- prcomp(t(subset_matrix_0.5[rowMeans(subset_matrix_0.5) > 0, ]), center = TRUE)
ggplot2::autoplot(prcomp_res_0.5, data = as.data.frame(subset_meta_0.5), colour = "Drug", shape = "Time", size = 4, x=3, y=4) +
ggrepel::geom_text_repel(label = subset_meta_0.5$Ind) +
scale_color_manual(values = drug_palc) +
ggtitle(expression("PCA of log"[2]*"(cpm) Filtered rowMeans > 0 (0.5 µM)")) +
theme_bw()
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] patchwork_1.3.0 ggfortify_0.4.17 org.Hs.eg.db_3.18.0
[4] AnnotationDbi_1.64.1 IRanges_2.36.0 S4Vectors_0.40.2
[7] Hmisc_5.2-3 corrplot_0.95 ggrepel_0.9.6
[10] biomaRt_2.58.2 scales_1.3.0 lubridate_1.9.4
[13] forcats_1.0.0 stringr_1.5.1 purrr_1.0.4
[16] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[19] tidyverse_2.0.0 Biobase_2.62.0 BiocGenerics_0.48.1
[22] dplyr_1.1.4 reshape2_1.4.4 ggplot2_3.5.2
[25] edgeR_4.0.16 limma_3.58.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-9 gridExtra_2.3
[4] rlang_1.1.3 magrittr_2.0.3 git2r_0.36.2
[7] compiler_4.3.0 RSQLite_2.3.9 getPass_0.2-4
[10] png_0.1-8 callr_3.7.6 vctrs_0.6.5
[13] pkgconfig_2.0.3 crayon_1.5.3 fastmap_1.2.0
[16] backports_1.5.0 dbplyr_2.5.0 XVector_0.42.0
[19] labeling_0.4.3 promises_1.3.2 rmarkdown_2.29
[22] tzdb_0.5.0 ps_1.8.1 bit_4.6.0
[25] xfun_0.52 zlibbioc_1.48.2 cachem_1.1.0
[28] GenomeInfoDb_1.38.8 jsonlite_2.0.0 progress_1.2.3
[31] blob_1.2.4 later_1.3.2 prettyunits_1.2.0
[34] cluster_2.1.8.1 R6_2.6.1 bslib_0.9.0
[37] stringi_1.8.3 rpart_4.1.24 jquerylib_0.1.4
[40] Rcpp_1.0.12 knitr_1.50 base64enc_0.1-3
[43] httpuv_1.6.15 nnet_7.3-20 timechange_0.3.0
[46] tidyselect_1.2.1 rstudioapi_0.17.1 yaml_2.3.10
[49] curl_6.2.2 processx_3.8.6 lattice_0.22-7
[52] plyr_1.8.9 withr_3.0.2 KEGGREST_1.42.0
[55] evaluate_1.0.3 foreign_0.8-90 BiocFileCache_2.10.2
[58] xml2_1.3.8 Biostrings_2.70.3 pillar_1.10.2
[61] filelock_1.0.3 whisker_0.4.1 checkmate_2.3.2
[64] generics_0.1.3 rprojroot_2.0.4 RCurl_1.98-1.17
[67] hms_1.1.3 munsell_0.5.1 glue_1.7.0
[70] tools_4.3.0 data.table_1.17.0 locfit_1.5-9.12
[73] fs_1.6.3 XML_3.99-0.18 grid_4.3.0
[76] colorspace_2.1-0 GenomeInfoDbData_1.2.11 htmlTable_2.4.3
[79] Formula_1.2-5 cli_3.6.1 rappdirs_0.3.3
[82] gtable_0.3.6 sass_0.4.10 digest_0.6.34
[85] farver_2.1.2 htmlwidgets_1.6.4 memoise_2.0.1
[88] htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7
[91] statmod_1.5.0 bit64_4.6.0-1