Cardiac and TOP2 Genes - Log2CPM Boxplots
Last updated: 2025-02-09
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Knit directory: CX5461_Project/
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| Rmd | c6b5ccd | sayanpaul01 | 2025-02-09 | Fixed boxplot significance issue for Cardiac and TOP2 genes |
📌 Log2CPM Boxplots for Cardiac and TOP2 Genes
This analysis generates boxplots for cardiac genes and TOP2 genes across different treatments and timepoints.
📌 Load Required Libraries
library(ggplot2)Warning: package 'ggplot2' was built under R version 4.3.3library(dplyr)Warning: package 'dplyr' was built under R version 4.3.2library(tidyr)Warning: package 'tidyr' was built under R version 4.3.3📌 Read Log2CPM Data
# 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")
cardiac_genes <- c("ACTN2", "CALR", "MYBPC3", "MYH6", "MYH7",
"MYL2", "RYR2", "SCN5A", "TNNI3", "TNNT2", "TTN")📌 Read and Process DEGs Data
# 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 Data for Plotting
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$Condition == Condition], na.rm = TRUE),
Significance = ifelse(is_significant(gene, Drug, Conc., Timepoint), "*", "")
) %>%
filter(Significance != "") %>% ungroup()
list(long_data = long_data, significance_labels = significance_labels)
}📌Generate Boxplots for Cardiac Genes
for (gene in cardiac_genes) {
data_info <- process_gene_data(gene)
p <- ggplot(data_info$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$significance_labels, aes(x = Condition, y = max_log2CPM + 0.5, label = Significance),
inherit.aes = FALSE, size = 6, color = "black") +
ggtitle(paste("Log2CPM Expression of", gene)) +
labs(x = "Treatment", y = "log2CPM") +
theme_bw() +
theme(plot.title = element_text(size = rel(2), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 90, hjust = 1))
print(p)
}
📌Generate Boxplots for TOP2 Genes
for (gene in top2_genes) {
data_info <- process_gene_data(gene)
p <- ggplot(data_info$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)
}
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] tidyr_1.3.1 dplyr_1.1.4 ggplot2_3.5.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_1.8.9 compiler_4.3.0 promises_1.3.0
[5] tidyselect_1.2.1 Rcpp_1.0.12 stringr_1.5.1 git2r_0.35.0
[9] callr_3.7.6 later_1.3.2 jquerylib_0.1.4 scales_1.3.0
[13] yaml_2.3.10 fastmap_1.1.1 R6_2.5.1 labeling_0.4.3
[17] generics_0.1.3 knitr_1.49 tibble_3.2.1 munsell_0.5.1
[21] rprojroot_2.0.4 bslib_0.8.0 pillar_1.10.1 rlang_1.1.3
[25] cachem_1.0.8 stringi_1.8.3 httpuv_1.6.15 xfun_0.50
[29] getPass_0.2-4 fs_1.6.3 sass_0.4.9 cli_3.6.1
[33] withr_3.0.2 magrittr_2.0.3 ps_1.8.1 digest_0.6.34
[37] grid_4.3.0 processx_3.8.5 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] whisker_0.4.1 colorspace_2.1-0 purrr_1.0.2 rmarkdown_2.29
[49] httr_1.4.7 tools_4.3.0 pkgconfig_2.0.3 htmltools_0.5.8.1