Figure_3
Last updated: 2025-08-06
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Knit directory: Paul_CX_2025/
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📌 DNA Damage Marker expression changes between CX5461 and DOX
# 📌 DDR Gene Expression Heatmap — CX-5461 and DOX (68 genes, with categories)
# Load Required Libraries
library(tidyverse)
library(ComplexHeatmap)
library(circlize)
library(grid)
library(org.Hs.eg.db)
library(reshape2)
# Load DEG files
load_deg <- function(path) read.csv(path)
CX_0.1_3 <- load_deg("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- load_deg("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- load_deg("data/DEGs/Toptable_CX_0.1_48.csv")
CX_0.5_3 <- load_deg("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- load_deg("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- load_deg("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.1_3 <- load_deg("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- load_deg("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- load_deg("data/DEGs/Toptable_DOX_0.1_48.csv")
DOX_0.5_3 <- load_deg("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- load_deg("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- load_deg("data/DEGs/Toptable_DOX_0.5_48.csv")
# 📌 Define Entrez IDs and Categories (excluding DOX Cardiotoxicity genes)
entrez_category <- tribble(
~ENTREZID, ~Category,
317, "Apoptosis", 355, "Apoptosis", 581, "Apoptosis", 637, "Apoptosis",
836, "Apoptosis", 841, "Apoptosis", 842, "Apoptosis", 27113, "Apoptosis",
5366, "Apoptosis", 54205, "Apoptosis", 55367, "Apoptosis", 8795, "Apoptosis",
1026, "Cell Cycle / Checkpoint", 1027, "Cell Cycle / Checkpoint", 595, "Cell Cycle / Checkpoint",
894, "Cell Cycle / Checkpoint", 896, "Cell Cycle / Checkpoint", 898, "Cell Cycle / Checkpoint",
9133, "Cell Cycle / Checkpoint", 9134, "Cell Cycle / Checkpoint", 891, "Cell Cycle / Checkpoint",
983, "Cell Cycle / Checkpoint", 1017, "Cell Cycle / Checkpoint", 1019, "Cell Cycle / Checkpoint",
1020, "Cell Cycle / Checkpoint", 1021, "Cell Cycle / Checkpoint", 993, "Cell Cycle / Checkpoint",
995, "Cell Cycle / Checkpoint", 1869, "Cell Cycle / Checkpoint", 4609, "Cell Cycle / Checkpoint",
5925, "Cell Cycle / Checkpoint", 9874, "Cell Cycle / Checkpoint", 11011, "Cell Cycle / Checkpoint",
1385, "Cell Cycle / Checkpoint",
472, "Damage Sensors / Signal Transducers", 545, "Damage Sensors / Signal Transducers",
5591, "Damage Sensors / Signal Transducers", 5810, "Damage Sensors / Signal Transducers",
5883, "Damage Sensors / Signal Transducers", 5884, "Damage Sensors / Signal Transducers",
6118, "Damage Sensors / Signal Transducers", 4361, "Damage Sensors / Signal Transducers",
10111, "Damage Sensors / Signal Transducers", 4683, "Damage Sensors / Signal Transducers",
84126, "Damage Sensors / Signal Transducers", 3014, "Damage Sensors / Signal Transducers",
672, "DNA Repair", 2177, "DNA Repair", 5888, "DNA Repair", 5893, "DNA Repair",
1647, "DNA Repair", 4616, "DNA Repair", 10912, "DNA Repair", 1111, "DNA Repair",
11200, "DNA Repair", 1643, "DNA Repair", 8243, "DNA Repair", 5981, "DNA Repair",
7157, "p53 Regulators / Targets", 4193, "p53 Regulators / Targets", 5371, "p53 Regulators / Targets",
27244, "p53 Regulators / Targets", 50484, "p53 Regulators / Targets",
207, "Miscellaneous / Broad", 25, "Miscellaneous / Broad"
)
entrez_ids <- entrez_category$ENTREZID
# 📌 Extract Data Function
extract_data <- function(df, name) {
df %>%
filter(Entrez_ID %in% entrez_ids) %>%
mutate(
Gene = mapIds(org.Hs.eg.db, as.character(Entrez_ID),
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Condition = name,
Signif = ifelse(adj.P.Val < 0.05, "*", "")
)
}
# DEG list
deg_list <- list(
"CX_0.1_3" = CX_0.1_3, "CX_0.1_24" = CX_0.1_24, "CX_0.1_48" = CX_0.1_48,
"CX_0.5_3" = CX_0.5_3, "CX_0.5_24" = CX_0.5_24, "CX_0.5_48" = CX_0.5_48,
"DOX_0.1_3" = DOX_0.1_3, "DOX_0.1_24" = DOX_0.1_24, "DOX_0.1_48" = DOX_0.1_48,
"DOX_0.5_3" = DOX_0.5_3, "DOX_0.5_24" = DOX_0.5_24, "DOX_0.5_48" = DOX_0.5_48
)
#Combine and Annotate
all_data <- bind_rows(mapply(extract_data, deg_list, names(deg_list), SIMPLIFY = FALSE)) %>%
left_join(entrez_category, by = c("Entrez_ID" = "ENTREZID"))
# Create matrices
logFC_mat <- acast(all_data, Gene ~ Condition, value.var = "logFC")
signif_mat <- acast(all_data, Gene ~ Condition, value.var = "Signif")
# Set column order
desired_order <- c("CX_0.1_3", "CX_0.1_24", "CX_0.1_48",
"CX_0.5_3", "CX_0.5_24", "CX_0.5_48",
"DOX_0.1_3", "DOX_0.1_24", "DOX_0.1_48",
"DOX_0.5_3", "DOX_0.5_24", "DOX_0.5_48")
logFC_mat <- logFC_mat[, desired_order, drop = FALSE]
signif_mat <- signif_mat[, desired_order, drop = FALSE]
# Column annotations
meta <- str_split_fixed(colnames(logFC_mat), "_", 3)
col_annot <- HeatmapAnnotation(
Drug = meta[, 1],
Conc = meta[, 2],
Time = meta[, 3],
col = list(
Drug = c("CX" = "blue", "DOX" = "red"),
Conc = c("0.1" = "lightgreen", "0.5" = "darkgreen"),
Time = c("3" = "yellow", "24" = "orange", "48" = "purple")
),
annotation_height = unit(c(1, 1, 1), "cm")
)
# Row annotations
gene_order_df <- all_data %>%
distinct(Gene, Category) %>%
arrange(factor(Category, levels = sort(unique(entrez_category$Category))), Gene)
ordered_genes <- gene_order_df$Gene
logFC_mat <- logFC_mat[ordered_genes, ]
signif_mat <- signif_mat[ordered_genes, ]
# Category color mapping
category_colors_named <- c(
"Apoptosis" = "darkorange",
"Cell Cycle / Checkpoint" = "steelblue",
"Damage Sensors / Signal Transducers" = "darkgreen",
"DNA Repair" = "firebrick",
"p53 Regulators / Targets" = "gold",
"Miscellaneous / Broad" = "gray60"
)
gene_order_df$Category <- factor(gene_order_df$Category, levels = names(category_colors_named))
ha_left <- rowAnnotation(
Category = gene_order_df$Category,
col = list(Category = category_colors_named),
annotation_name_side = "top"
)
# Final Heatmap
Heatmap(logFC_mat,
name = "logFC",
top_annotation = col_annot,
left_annotation = ha_left,
cluster_columns = FALSE,
cluster_rows = FALSE,
show_row_names = TRUE,
show_column_names = FALSE,
row_names_gp = gpar(fontsize = 10),
column_title = "DDR Gene Expression Response (n = 65)\nCX-5461 and DOX",
column_title_gp = gpar(fontsize = 14, fontface = "bold"),
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(signif_mat[i, j], x, y, gp = gpar(fontsize = 9))
}
)
📌 Log2CPM Boxplots for TOP2 Genes and TP53
library(ggplot2)
library(dplyr)
library(tidyr)
library(org.Hs.eg.db)
library(clusterProfiler)Warning: package 'clusterProfiler' was built under R version 4.3.3# Load feature count matrix
boxplot1 <- read.csv("data/Feature_count_Matrix_Log2CPM_filtered.csv") %>% as.data.frame()
# Ensure column names are cleaned
colnames(boxplot1) <- trimws(gsub("^X", "", colnames(boxplot1)))
## **📌 Define Genes of Interest**
# Define the genes of interest
top2_genes <- c("TOP2A", "TOP2B")
dna_damage_genes <- c("TP53") # Using correct gene symbol TP53
# Load Toptables
deg_files <- list.files("data/DEGs", pattern = "Toptable_.*\\.csv", full.names = TRUE)
deg_list <- lapply(deg_files, read.csv)
names(deg_list) <- gsub("data/DEGs/Toptable_|\\.csv", "", deg_files)
# Function to check significance based on **Entrez_ID in the correct sample**
is_significant <- function(gene, drug, conc, timepoint) {
condition <- paste(drug, conc, timepoint, sep = "_")
if (!condition %in% names(deg_list)) return(FALSE)
toptable <- deg_list[[condition]]
gene_entrez <- boxplot1$ENTREZID[boxplot1$SYMBOL == gene]
if (length(gene_entrez) == 0) return(FALSE)
return(any(gene_entrez %in% toptable$Entrez_ID[toptable$adj.P.Val < 0.05]))
}
process_gene_data <- function(gene) {
# Filter log2CPM data for the gene
gene_data <- boxplot1 %>% filter(SYMBOL == gene)
# Reshape data
long_data <- gene_data %>%
pivot_longer(cols = -c(ENTREZID, SYMBOL, GENENAME), names_to = "Sample", values_to = "log2CPM") %>%
mutate(
Indv = case_when(
grepl("75.1", Sample) ~ "1",
grepl("78.1", Sample) ~ "2",
grepl("87.1", Sample) ~ "3",
grepl("17.3", Sample) ~ "4",
grepl("84.1", Sample) ~ "5",
grepl("90.1", Sample) ~ "6",
TRUE ~ NA_character_
),
Drug = case_when(
grepl("CX.5461", Sample) ~ "CX",
grepl("DOX", Sample) ~ "DOX",
grepl("VEH", Sample) ~ "VEH",
TRUE ~ NA_character_
),
Conc. = case_when(
grepl("_0.1_", Sample) ~ "0.1",
grepl("_0.5_", Sample) ~ "0.5",
TRUE ~ NA_character_
),
Timepoint = case_when(
grepl("_3$", Sample) ~ "3",
grepl("_24$", Sample) ~ "24",
grepl("_48$", Sample) ~ "48",
TRUE ~ NA_character_
),
Condition = paste(Drug, Conc., Timepoint, sep = "_")
)
# **Ensure Condition is Ordered Correctly**
long_data$Condition <- factor(
long_data$Condition,
levels = c(
"CX_0.1_3", "CX_0.1_24", "CX_0.1_48", "CX_0.5_3", "CX_0.5_24", "CX_0.5_48",
"DOX_0.1_3", "DOX_0.1_24", "DOX_0.1_48", "DOX_0.5_3", "DOX_0.5_24", "DOX_0.5_48",
"VEH_0.1_3", "VEH_0.1_24", "VEH_0.1_48", "VEH_0.5_3", "VEH_0.5_24", "VEH_0.5_48"
)
)
# Identify significant conditions **per Drug, Conc, and Timepoint**
significance_labels <- long_data %>%
distinct(Drug, Conc., Timepoint, Condition) %>%
rowwise() %>%
mutate(
max_log2CPM = max(long_data$log2CPM[long_data$Condition == Condition], na.rm = TRUE),
Significance = ifelse(is_significant(gene, Drug, Conc., Timepoint), "*", "")
) %>%
filter(Significance != "") %>% ungroup()
list(long_data = long_data, significance_labels = significance_labels)
}
for (gene in top2_genes) {
data_info <- process_gene_data(gene)
p <- ggplot(data_info$long_data, aes(x = Condition, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = c("CX" = "#0000FF", "DOX" = "#e6d800", "VEH" = "#FF00FF")) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5, position = position_jitter(width = -1, height = 0)) +
geom_text(data = data_info$significance_labels, aes(x = Condition, y = max_log2CPM + 0.5, label = Significance),
inherit.aes = FALSE, size = 6, color = "black") +
ggtitle(paste("Log2CPM Expression of", gene)) +
labs(x = "Treatment", y = "log2CPM") +
theme_bw() +
theme(plot.title = element_text(size = rel(2), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 90, hjust = 1))
print(p)
}
for (gene in dna_damage_genes) {
data_info <- process_gene_data(gene)
p <- ggplot(data_info$long_data, aes(x = Condition, y = log2CPM, fill = Drug)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = c("CX" = "#0000FF", "DOX" = "#e6d800", "VEH" = "#FF00FF")) +
geom_point(aes(color = Indv), size = 2, alpha = 0.5, position = position_jitter(width = -1, height = 0)) +
geom_text(data = data_info$significance_labels, aes(x = Condition, y = max_log2CPM + 0.5, label = Significance),
inherit.aes = FALSE, size = 6, color = "black") +
ggtitle(paste("Log2CPM Expression of", gene)) +
labs(x = "Treatment", y = "log2CPM") +
theme_bw() +
theme(plot.title = element_text(size = rel(2), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 90, hjust = 1))
print(p)
}
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 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] clusterProfiler_4.10.1 reshape2_1.4.4 org.Hs.eg.db_3.18.0
[4] AnnotationDbi_1.64.1 IRanges_2.36.0 S4Vectors_0.40.2
[7] Biobase_2.62.0 BiocGenerics_0.48.1 circlize_0.4.16
[10] ComplexHeatmap_2.18.0 lubridate_1.9.4 forcats_1.0.0
[13] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4
[16] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[19] ggplot2_3.5.2 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_2.0.0
[4] shape_1.4.6.1 magrittr_2.0.3 magick_2.8.6
[7] farver_2.1.2 rmarkdown_2.29 GlobalOptions_0.1.2
[10] fs_1.6.3 zlibbioc_1.48.2 vctrs_0.6.5
[13] memoise_2.0.1 Cairo_1.6-2 RCurl_1.98-1.17
[16] ggtree_3.10.1 htmltools_0.5.8.1 gridGraphics_0.5-1
[19] sass_0.4.10 bslib_0.9.0 plyr_1.8.9
[22] cachem_1.1.0 igraph_2.1.4 lifecycle_1.0.4
[25] iterators_1.0.14 pkgconfig_2.0.3 gson_0.1.0
[28] Matrix_1.6-1.1 R6_2.6.1 fastmap_1.2.0
[31] GenomeInfoDbData_1.2.11 clue_0.3-66 digest_0.6.34
[34] aplot_0.2.5 enrichplot_1.22.0 colorspace_2.1-0
[37] patchwork_1.3.0 rprojroot_2.0.4 RSQLite_2.3.9
[40] labeling_0.4.3 timechange_0.3.0 httr_1.4.7
[43] polyclip_1.10-7 compiler_4.3.0 bit64_4.6.0-1
[46] withr_3.0.2 doParallel_1.0.17 BiocParallel_1.36.0
[49] viridis_0.6.5 DBI_1.2.3 ggforce_0.4.2
[52] MASS_7.3-60 rjson_0.2.23 HDO.db_0.99.1
[55] tools_4.3.0 scatterpie_0.2.4 ape_5.8-1
[58] httpuv_1.6.15 glue_1.7.0 nlme_3.1-168
[61] GOSemSim_2.28.1 promises_1.3.2 shadowtext_0.1.4
[64] cluster_2.1.8.1 fgsea_1.28.0 generics_0.1.3
[67] gtable_0.3.6 tzdb_0.5.0 data.table_1.17.0
[70] hms_1.1.3 tidygraph_1.3.1 XVector_0.42.0
[73] ggrepel_0.9.6 foreach_1.5.2 pillar_1.10.2
[76] yulab.utils_0.2.0 later_1.3.2 splines_4.3.0
[79] tweenr_2.0.3 treeio_1.26.0 lattice_0.22-7
[82] bit_4.6.0 tidyselect_1.2.1 GO.db_3.18.0
[85] Biostrings_2.70.3 knitr_1.50 git2r_0.36.2
[88] gridExtra_2.3 xfun_0.52 graphlayouts_1.2.2
[91] matrixStats_1.5.0 stringi_1.8.3 lazyeval_0.2.2
[94] workflowr_1.7.1 ggfun_0.1.8 yaml_2.3.10
[97] evaluate_1.0.3 codetools_0.2-20 ggraph_2.2.1
[100] qvalue_2.34.0 ggplotify_0.1.2 cli_3.6.1
[103] munsell_0.5.1 jquerylib_0.1.4 Rcpp_1.0.12
[106] GenomeInfoDb_1.38.8 png_0.1-8 parallel_4.3.0
[109] blob_1.2.4 DOSE_3.28.2 bitops_1.0-9
[112] viridisLite_0.4.2 tidytree_0.4.6 scales_1.3.0
[115] crayon_1.5.3 GetoptLong_1.0.5 rlang_1.1.3
[118] cowplot_1.1.3 fastmatch_1.1-6 KEGGREST_1.42.0