Cardiac and TOP2 Genes - Log2CPM Boxplots
Last updated: 2025-02-18
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Knit directory: CX5461_Project/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 55dbfa0 | sayanpaul01 | 2025-02-18 | Build site. |
| Rmd | 1b220ad | sayanpaul01 | 2025-02-18 | Updated analysis of Cardiac, TOP2, and DNA Damage Genes |
| html | 0a7f53e | sayanpaul01 | 2025-02-18 | Build site. |
| Rmd | 565ede2 | sayanpaul01 | 2025-02-18 | Updated analysis of Cardiac, TOP2, and DNA Damage Genes |
| html | 3313173 | sayanpaul01 | 2025-02-18 | Build site. |
| html | 456790a | sayanpaul01 | 2025-02-18 | Build site. |
| Rmd | e943e2d | sayanpaul01 | 2025-02-18 | Added TP53 as a DNA damage marker in log2CPM boxplots |
| html | 6ed78e2 | sayanpaul01 | 2025-02-09 | Build site. |
| html | 72599ac | sayanpaul01 | 2025-02-09 | Build site. |
| Rmd | 0e79f5b | sayanpaul01 | 2025-02-09 | Fix Timepoint Ordering in Boxplots |
| html | a41bd50 | sayanpaul01 | 2025-02-09 | Build site. |
| Rmd | 91f5f8f | sayanpaul01 | 2025-02-09 | Fix Timepoint Ordering in Boxplots |
| html | bcc58f6 | sayanpaul01 | 2025-02-09 | Build site. |
| html | 8c1912f | sayanpaul01 | 2025-02-09 | Build site. |
| 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 4.3.3library(org.Hs.eg.db)Warning: package 'AnnotationDbi' was built under R version 4.3.2Warning: package 'BiocGenerics' was built under R version 4.3.1Warning: package 'Biobase' was built under R version 4.3.1Warning: package 'IRanges' was built under R version 4.3.1Warning: package 'S4Vectors' was built under R version 4.3.1library(clusterProfiler)Warning: package 'clusterProfiler' 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")
dna_damage_genes <- c("TP53") # Using correct gene symbol TP53📌 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$significance_labels, aes(x = Condition, y = max_log2CPM + 0.5, label = Significance),
inherit.aes = FALSE, size = 6, color = "black") +
ggtitle(paste("Log2CPM Expression of", gene)) +
labs(x = "Treatment", y = "log2CPM") +
theme_bw() +
theme(plot.title = element_text(size = rel(2), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 90, hjust = 1))
print(p)
}
📌 Generate Boxplots for DNA Damage Genes
for (gene in dna_damage_genes) {
data_info <- process_gene_data(gene)
p <- ggplot(data_info$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$significance_labels, aes(x = Condition, y = max_log2CPM + 0.5, label = Significance),
inherit.aes = FALSE, size = 6, color = "black") +
ggtitle(paste("Log2CPM Expression of", gene)) +
labs(x = "Treatment", y = "log2CPM") +
theme_bw() +
theme(plot.title = element_text(size = rel(2), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, color = "black", angle = 90, hjust = 1))
print(p)
}
| Version | Author | Date |
|---|---|---|
| 456790a | sayanpaul01 | 2025-02-18 |
##📌 DNA Damage Proportion
📌 Read and Process DEG Data
# Load DEGs Data
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Extract Significant DEGs
DEGs <- list(
"CX_0.1_3" = CX_0.1_3$Entrez_ID[CX_0.1_3$adj.P.Val < 0.05],
"CX_0.1_24" = CX_0.1_24$Entrez_ID[CX_0.1_24$adj.P.Val < 0.05],
"CX_0.1_48" = CX_0.1_48$Entrez_ID[CX_0.1_48$adj.P.Val < 0.05],
"CX_0.5_3" = CX_0.5_3$Entrez_ID[CX_0.5_3$adj.P.Val < 0.05],
"CX_0.5_24" = CX_0.5_24$Entrez_ID[CX_0.5_24$adj.P.Val < 0.05],
"CX_0.5_48" = CX_0.5_48$Entrez_ID[CX_0.5_48$adj.P.Val < 0.05],
"DOX_0.1_3" = DOX_0.1_3$Entrez_ID[DOX_0.1_3$adj.P.Val < 0.05],
"DOX_0.1_24" = DOX_0.1_24$Entrez_ID[DOX_0.1_24$adj.P.Val < 0.05],
"DOX_0.1_48" = DOX_0.1_48$Entrez_ID[DOX_0.1_48$adj.P.Val < 0.05],
"DOX_0.5_3" = DOX_0.5_3$Entrez_ID[DOX_0.5_3$adj.P.Val < 0.05],
"DOX_0.5_24" = DOX_0.5_24$Entrez_ID[DOX_0.5_24$adj.P.Val < 0.05],
"DOX_0.5_48" = DOX_0.5_48$Entrez_ID[DOX_0.5_48$adj.P.Val < 0.05]
)
# Extract Significant DEGs
DEG1 <- CX_0.1_3$Entrez_ID[CX_0.1_3$adj.P.Val < 0.05]
DEG2 <- CX_0.1_24$Entrez_ID[CX_0.1_24$adj.P.Val < 0.05]
DEG3 <- CX_0.1_48$Entrez_ID[CX_0.1_48$adj.P.Val < 0.05]
DEG4 <- CX_0.5_3$Entrez_ID[CX_0.5_3$adj.P.Val < 0.05]
DEG5 <- CX_0.5_24$Entrez_ID[CX_0.5_24$adj.P.Val < 0.05]
DEG6 <- CX_0.5_48$Entrez_ID[CX_0.5_48$adj.P.Val < 0.05]
DEG7 <- DOX_0.1_3$Entrez_ID[DOX_0.1_3$adj.P.Val < 0.05]
DEG8 <- DOX_0.1_24$Entrez_ID[DOX_0.1_24$adj.P.Val < 0.05]
DEG9 <- DOX_0.1_48$Entrez_ID[DOX_0.1_48$adj.P.Val < 0.05]
DEG10 <- DOX_0.5_3$Entrez_ID[DOX_0.5_3$adj.P.Val < 0.05]
DEG11 <- DOX_0.5_24$Entrez_ID[DOX_0.5_24$adj.P.Val < 0.05]
DEG12 <- DOX_0.5_48$Entrez_ID[DOX_0.5_48$adj.P.Val < 0.05]📌 DNA Damage Proportion with Chi-Square Test
# Load DNA Damage Genes List
DNA_damage <- read.csv("data/DNA_Damage.csv", stringsAsFactors = FALSE)
DNA_damage$Entrez_ID <- mapIds(org.Hs.eg.db,
keys = DNA_damage$Symbol,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first")
# Define CX-5461 DEG lists
CX_DEGs <- list(
"CX_0.1_3" = DEG1, "CX_0.1_24" = DEG2, "CX_0.1_48" = DEG3,
"CX_0.5_3" = DEG4, "CX_0.5_24" = DEG5, "CX_0.5_48" = DEG6
)
# Define DOX DEG lists
DOX_DEGs <- list(
"DOX_0.1_3" = DEG7, "DOX_0.1_24" = DEG8, "DOX_0.1_48" = DEG9,
"DOX_0.5_3" = DEG10, "DOX_0.5_24" = DEG11, "DOX_0.5_48" = DEG12
)
# Extract Entrez_IDs from DNA Damage gene dataset
DNA_damage_genes <- na.omit(DNA_damage$Entrez_ID)
# Combine CX-5461 DEGs into a dataframe with a "Drug" column
CX_DEGs_df <- bind_rows(
lapply(CX_DEGs, function(ids) data.frame(Entrez_ID = ids, Drug = "CX-5461")),
.id = "Sample"
)
# Combine DOX DEGs into a dataframe with a "Drug" column
DOX_DEGs_df <- bind_rows(
lapply(DOX_DEGs, function(ids) data.frame(Entrez_ID = ids, Drug = "DOX")),
.id = "Sample"
)
# Merge CX-5461 and DOX datasets
DEGs_df <- bind_rows(CX_DEGs_df, DOX_DEGs_df)
# Check if genes are in DNA Damage list
DEGs_df <- DEGs_df %>%
mutate(Category = ifelse(Entrez_ID %in% DNA_damage_genes, "Yes", "No"))
# Count DNA damage genes in each sample
proportion_data <- DEGs_df %>%
group_by(Sample, Drug, Category) %>%
summarise(Count = n(), .groups = "drop") %>%
group_by(Sample, Drug) %>%
mutate(Percentage = (Count / sum(Count)) * 100)
# Normalize Percentages to Sum Exactly 100%
proportion_data <- proportion_data %>%
group_by(Sample) %>%
mutate(Percentage = round(Percentage, 2)) %>%
mutate(Adjustment = 100 - sum(Percentage, na.rm = TRUE)) %>%
mutate(Percentage = ifelse(Category == "No", Percentage + Adjustment, Percentage)) %>%
mutate(Percentage = ifelse(Percentage < 0, 0, Percentage)) %>%
mutate(Percentage = ifelse(Percentage > 100, 100, Percentage)) %>%
ungroup() %>%
replace_na(list(Percentage = 0))
# Ensure "Yes" is at the Bottom and "No" is at the Top
proportion_data$Category <- factor(proportion_data$Category, levels = c("Yes", "No"))
# **🔹 Maintain Correct X-Axis Order (3 → 24 → 48)**
sample_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"
)
proportion_data$Sample <- factor(proportion_data$Sample, levels = sample_order, ordered = TRUE)
# **Perform Chi-Square Test for CX vs DOX Pairs**
chi_square_results <- data.frame(Sample = character(), P_Value = numeric())
for (i in seq(1, 6)) { # Pairwise comparison (CX vs DOX)
cx_sample <- sample_order[i]
dox_sample <- sample_order[i + 6] # Correctly pairs CX_0.1_3 with DOX_0.1_3, etc.
cx_data <- filter(proportion_data, Sample == cx_sample)
dox_data <- filter(proportion_data, Sample == dox_sample)
# Construct contingency table for Chi-Square test
contingency_table <- matrix(
c(sum(cx_data$Count[cx_data$Category == "Yes"]), sum(cx_data$Count[cx_data$Category == "No"]),
sum(dox_data$Count[dox_data$Category == "Yes"]), sum(dox_data$Count[dox_data$Category == "No"])),
nrow = 2, byrow = TRUE
)
# Run Chi-Square Test
test_result <- chisq.test(contingency_table)
p_value <- test_result$p.value
# Store results
chi_square_results <- rbind(chi_square_results, data.frame(Sample = cx_sample, P_Value = p_value))
}
# Identify significant CX samples (p < 0.05)
chi_square_results$Significant <- ifelse(chi_square_results$P_Value < 0.05, "*", "")
# **🔹 Merge Chi-Square Results WITHOUT Modifying Order**
proportion_data <- left_join(proportion_data, chi_square_results, by = "Sample")
# **Reapply Factor Order to Prevent Changes**
proportion_data$Sample <- factor(proportion_data$Sample, levels = sample_order, ordered = TRUE)📌 DNA Damage Proportion Plot
# **Generate Proportion Plot for CX-5461 and DOX**
ggplot(proportion_data, aes(x = Sample, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
geom_text(data = subset(proportion_data, Significant == "*"),
aes(x = Sample, y = 102, label = "*"),
size = 6, color = "black", fontface = "bold") +
scale_y_continuous(labels = scales::percent_format(scale = 1), limits = c(0, 105)) +
scale_fill_manual(values = c("Yes" = "#e41a1c", "No" = "#377eb8")) +
labs(
title = "Proportion of DNA Damage Genes in CX-5461 and DOX DEGs\nwith Significance",
x = "Samples (CX-5461 and DOX)",
y = "Percentage",
fill = "Category"
) +
theme_minimal() +
theme(
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
legend.title = element_blank(),
panel.border = element_rect(color = "black", fill = NA, linewidth = 1.2),
strip.background = element_blank(),
strip.text = element_text(size = 12, face = "bold")
)
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] clusterProfiler_4.10.1 org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1
[4] IRanges_2.36.0 S4Vectors_0.40.1 Biobase_2.62.0
[7] BiocGenerics_0.48.1 tidyr_1.3.1 dplyr_1.1.4
[10] ggplot2_3.5.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-7 gson_0.1.0
[4] shadowtext_0.1.4 gridExtra_2.3 rlang_1.1.3
[7] magrittr_2.0.3 DOSE_3.28.2 git2r_0.35.0
[10] compiler_4.3.0 RSQLite_2.3.3 getPass_0.2-4
[13] png_0.1-8 callr_3.7.6 vctrs_0.6.5
[16] reshape2_1.4.4 stringr_1.5.1 pkgconfig_2.0.3
[19] crayon_1.5.3 fastmap_1.1.1 XVector_0.42.0
[22] labeling_0.4.3 ggraph_2.2.1 HDO.db_0.99.1
[25] promises_1.3.0 rmarkdown_2.29 enrichplot_1.22.0
[28] ps_1.8.1 purrr_1.0.2 bit_4.0.5
[31] xfun_0.50 zlibbioc_1.48.0 cachem_1.0.8
[34] aplot_0.2.3 GenomeInfoDb_1.38.8 jsonlite_1.8.9
[37] blob_1.2.4 later_1.3.2 BiocParallel_1.36.0
[40] tweenr_2.0.3 parallel_4.3.0 R6_2.5.1
[43] RColorBrewer_1.1-3 bslib_0.8.0 stringi_1.8.3
[46] jquerylib_0.1.4 GOSemSim_2.28.1 Rcpp_1.0.12
[49] knitr_1.49 httpuv_1.6.15 Matrix_1.6-1.1
[52] splines_4.3.0 igraph_2.1.1 tidyselect_1.2.1
[55] viridis_0.6.5 qvalue_2.34.0 rstudioapi_0.17.1
[58] yaml_2.3.10 codetools_0.2-20 processx_3.8.5
[61] lattice_0.22-5 tibble_3.2.1 plyr_1.8.9
[64] treeio_1.26.0 withr_3.0.2 KEGGREST_1.42.0
[67] evaluate_1.0.3 gridGraphics_0.5-1 scatterpie_0.2.4
[70] polyclip_1.10-7 Biostrings_2.70.1 ggtree_3.10.1
[73] pillar_1.10.1 whisker_0.4.1 ggfun_0.1.8
[76] generics_0.1.3 rprojroot_2.0.4 RCurl_1.98-1.13
[79] tidytree_0.4.6 munsell_0.5.1 scales_1.3.0
[82] glue_1.7.0 lazyeval_0.2.2 tools_4.3.0
[85] data.table_1.14.10 fgsea_1.28.0 fs_1.6.3
[88] graphlayouts_1.2.0 fastmatch_1.1-4 tidygraph_1.3.1
[91] cowplot_1.1.3 grid_4.3.0 ape_5.8
[94] colorspace_2.1-0 nlme_3.1-166 patchwork_1.3.0
[97] GenomeInfoDbData_1.2.11 ggforce_0.4.2 cli_3.6.1
[100] viridisLite_0.4.2 gtable_0.3.6 yulab.utils_0.1.8
[103] sass_0.4.9 digest_0.6.34 ggplotify_0.1.2
[106] ggrepel_0.9.6 farver_2.1.2 memoise_2.0.1
[109] htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7
[112] GO.db_3.18.0 bit64_4.0.5 MASS_7.3-60