Figure_S8
Last updated: 2025-08-10
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Knit directory: Paul_CX_2025/
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📌PC1–PC2 Gene Expression Variance Across Individual, Drug, Concentration, and Timepoint log2(CPM) (RowMeans > 0)
# 📌 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(dplyr)Warning: package 'dplyr' was built under R version 4.3.2library(tidyr)Warning: package 'tidyr' was built under R version 4.3.3library(ggrepel)Warning: package 'ggrepel' was built under R version 4.3.3library(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"))
# 📌 PCA
prcomp_res <- prcomp(t(matrix), center = TRUE)
pca_df <- as.data.frame(prcomp_res$x[, 1:2]) # ✅ Only PC1–PC2
pca_df$Ind <- Metadata$Ind
pca_df$Drug <- Metadata$Drug
pca_df$Conc <- Metadata$`Conc.`
pca_df$Time <- Metadata$Time
pca_df$Sex <- Metadata$Sex
# 📌 p-value from linear model
get_regr_pval <- function(mod) {
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$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 plots: PC1–PC2 × Ind, Drug, Conc, Time, Sex
pcs <- c("PC1", "PC2") # ✅ No PC3
group_vars <- c("Ind", "Drug", "Conc", "Time", "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"))
# 📌 Assemble 5-column layout with 2 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"]])
) + plot_layout(heights = c(0.07, 1, 1))
# 📌 Display the plot
print(final_plot)
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.3.0 ggrepel_0.9.6 tidyr_1.3.1 dplyr_1.1.4
[5] ggplot2_3.5.2 edgeR_4.0.16 limma_3.58.1
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_2.0.0 compiler_4.3.0 promises_1.3.2
[5] tidyselect_1.2.1 Rcpp_1.0.12 stringr_1.5.1 git2r_0.36.2
[9] later_1.3.2 jquerylib_0.1.4 scales_1.3.0 yaml_2.3.10
[13] fastmap_1.2.0 statmod_1.5.0 lattice_0.22-7 R6_2.6.1
[17] labeling_0.4.3 generics_0.1.3 workflowr_1.7.1 knitr_1.50
[21] tibble_3.2.1 munsell_0.5.1 rprojroot_2.0.4 bslib_0.9.0
[25] pillar_1.10.2 rlang_1.1.3 cachem_1.1.0 stringi_1.8.3
[29] httpuv_1.6.15 xfun_0.52 fs_1.6.3 sass_0.4.10
[33] cli_3.6.1 withr_3.0.2 magrittr_2.0.3 digest_0.6.34
[37] grid_4.3.0 locfit_1.5-9.12 rstudioapi_0.17.1 lifecycle_1.0.4
[41] vctrs_0.6.5 evaluate_1.0.3 glue_1.7.0 farver_2.1.2
[45] colorspace_2.1-0 purrr_1.0.4 rmarkdown_2.29 tools_4.3.0
[49] pkgconfig_2.0.3 htmltools_0.5.8.1