Genes in AC toxicity-associated loci response to CX5461 and DOX
Last updated: 2025-04-24
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 693aad4 | sayanpaul01 | 2025-04-20 | Build site. |
| html | 0d15a95 | sayanpaul01 | 2025-04-20 | Commit |
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| html | 910b6fb | sayanpaul01 | 2025-04-20 | Commit |
📌 AC Cardiotoxicity
📌 Load Required Libraries
library(tidyverse)
library(ggfortify)
library(cluster)
library(edgeR)
library(limma)
library(Homo.sapiens)
library(BiocParallel)
library(qvalue)
library(pheatmap)
library(clusterProfiler)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(RColorBrewer)
library(readr)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ComplexHeatmap)
library(circlize)
library(grid)
library(reshape2)
library(dplyr)
# Load UCSC transcript database
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene📌 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")
Entrez_IDs <- c(
6272, 8029, 11128, 79899, 54477, 121665, 5095, 22863, 57161, 4692,
8214, 23151, 56606, 108, 22999, 56895, 9603, 3181, 4023, 10499,
92949, 4363, 10057, 5243, 5244, 5880, 1535, 2950, 847, 5447,
3038, 3077, 4846, 3958, 23327, 29899, 23155, 80856, 55020, 78996,
150383, 79730, 344595, 6251, 3482, 23262, 9588, 87769, 23409, 339416, 7292, 55157, 720, 5066, 3107, 54535, 1590, 80059, 7991, 57110, 8803, 9620, 323, 54826, 5916, 23371, 283337, 64078, 80010, 1933, 10818, 51020, 873, 874, 2064, 2878, 2944, 51196, 6687, 7799, 4292, 51310, 9154, 10060, 89845, 56853, 4625, 1573, 79890
)
# Subset the toptable based on the entrez IDs and select specific columns
subset_toptable1 <- CX_0.1_3[CX_0.1_3$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable2 <- CX_0.1_24[CX_0.1_24$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable3 <- CX_0.1_48[CX_0.1_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable3 <- CX_0.1_48[CX_0.1_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable4 <- CX_0.5_3[CX_0.5_3$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable5 <- CX_0.5_24[CX_0.5_24$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable6 <- CX_0.5_48[CX_0.5_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable7 <- DOX_0.1_3[DOX_0.1_3$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable8 <- DOX_0.1_24[DOX_0.1_24$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable9 <- DOX_0.1_48[DOX_0.1_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable10 <- DOX_0.5_3[DOX_0.5_3$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable11 <- DOX_0.5_24[DOX_0.5_24$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable12 <- DOX_0.5_48[DOX_0.5_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]📌 Add metadata and combine all subsets
📌Create a matrix and heatmap
# Assuming your dataframe is named data
# Add a column for significance stars
final_data <- final_data %>%
mutate(Significance = ifelse(adj.P.Val < 0.05, "*", ""))
# Create a matrix for the heatmap (logFC values)
logFC_matrix <- acast(final_data, Gene ~ paste(Drug, Conc, Time, sep = "_"), value.var = "logFC")
# Create a matrix for the significance annotations
signif_matrix <- acast(final_data, Gene ~ paste(Drug, Conc, Time, sep = "_"), value.var = "Significance")
# Split column names into Drug, Conc, and Time
colnames_split <- strsplit(colnames(logFC_matrix), "_")
drug <- sapply(colnames_split, function(x) x[1])
conc <- sapply(colnames_split, function(x) x[2])
time <- sapply(colnames_split, function(x) x[3])
# Create the desired column order: CX 0.1 3hr, CX 0.5 3hr, CX 0.1 24hr, CX 0.5 24hr, CX 0.1 48h, CX 0.5 48h,
# DOX 0.1 3hr, DOX 0.5 3hr, DOX 0.1 24hr, DOX 0.5 24hr, DOX 0.1 48h, DOX 0.5 48h
desired_order <- c("CX_0.1_3", "CX_0.5_3", "CX_0.1_24", "CX_0.5_24", "CX_0.1_48", "CX_0.5_48",
"DOX_0.1_3", "DOX_0.5_3", "DOX_0.1_24", "DOX_0.5_24", "DOX_0.1_48", "DOX_0.5_48")
# Reorder columns in the matrix based on the desired order
column_names <- paste(drug, conc, time, sep = "_")
column_order <- match(desired_order, column_names)
logFC_matrix <- logFC_matrix[, column_order]
signif_matrix <- signif_matrix[, column_order]
drug <- drug[column_order]
conc <- conc[column_order]
time <- time[column_order]
# Prepare annotations matching the column structure
ha_top <- HeatmapAnnotation(
Drug = drug,
Conc = conc,
Time = time,
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(2, 2, 2), "cm")
)
# Create the heatmap
heatmap <- Heatmap(logFC_matrix, name = "logFC", top_annotation = ha_top,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(signif_matrix[i, j], x, y, gp = gpar(fontsize = 10))
},
show_row_names = TRUE, show_column_names = FALSE,
column_title = "Genes in AC toxicity-associated loci\nresponse to CX5461 and DOX",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
cluster_columns = FALSE) # Disable column clustering
# Draw the heatmap
draw(heatmap, heatmap_legend_side = "right", annotation_legend_side = "right")
| Version | Author | Date |
|---|---|---|
| 910b6fb | sayanpaul01 | 2025-04-20 |
📌 DOX Cardiotoxicity
# 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")
Entrez_IDs <- c(
847, 873, 2064, 2878, 2944, 3038, 4846, 51196, 5880, 6687,
7799, 4292, 5916, 3077, 51310, 9154, 64078, 5244, 10057, 10060,
89845, 56853, 4625, 1573, 79890
)
# Subset the toptable based on the entrez IDs and select specific columns
subset_toptable1 <- CX_0.1_3[CX_0.1_3$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable2 <- CX_0.1_24[CX_0.1_24$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable3 <- CX_0.1_48[CX_0.1_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable3 <- CX_0.1_48[CX_0.1_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable4 <- CX_0.5_3[CX_0.5_3$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable5 <- CX_0.5_24[CX_0.5_24$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable6 <- CX_0.5_48[CX_0.5_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable7 <- DOX_0.1_3[DOX_0.1_3$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable8 <- DOX_0.1_24[DOX_0.1_24$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable9 <- DOX_0.1_48[DOX_0.1_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable10 <- DOX_0.5_3[DOX_0.5_3$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable11 <- DOX_0.5_24[DOX_0.5_24$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
subset_toptable12 <- DOX_0.5_48[DOX_0.5_48$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
# Function to add columns and combine data
add_metadata <- function(data, drug, conc, time) {
data %>%
mutate(Drug = drug, Conc = conc, Time = time)
}
# Add metadata and combine all subsets
combined_data <- bind_rows(
add_metadata(subset_toptable1, "CX", 0.1, 3),
add_metadata(subset_toptable2, "CX", 0.1, 24),
add_metadata(subset_toptable3, "CX", 0.1, 48),
add_metadata(subset_toptable4, "CX", 0.5, 3),
add_metadata(subset_toptable5, "CX", 0.5, 24),
add_metadata(subset_toptable6, "CX", 0.5, 48),
add_metadata(subset_toptable7, "DOX", 0.1, 3),
add_metadata(subset_toptable8, "DOX", 0.1, 24),
add_metadata(subset_toptable9, "DOX", 0.1, 48),
add_metadata(subset_toptable10, "DOX", 0.5, 3),
add_metadata(subset_toptable11, "DOX", 0.5, 24),
add_metadata(subset_toptable12, "DOX", 0.5, 48)
)
# Convert Entrez IDs to Gene symbols
combined_data <- combined_data %>%
mutate(Gene = mapIds(
org.Hs.eg.db,
keys = as.character(Entrez_ID),
column = "SYMBOL",
keytype = "ENTREZID",
multiVals = "first"
))
# Reorder columns
final_data <- dplyr::select(combined_data, Entrez_ID, Gene, logFC, adj.P.Val, Drug, Conc, Time)
# Assuming your dataframe is named data
# Add a column for significance stars
final_data <- final_data %>%
mutate(Significance = ifelse(adj.P.Val < 0.05, "*", ""))
# Create a matrix for the heatmap (logFC values)
logFC_matrix <- acast(final_data, Gene ~ paste(Drug, Conc, Time, sep = "_"), value.var = "logFC")
# Create a matrix for the significance annotations
signif_matrix <- acast(final_data, Gene ~ paste(Drug, Conc, Time, sep = "_"), value.var = "Significance")
# Split column names into Drug, Conc, and Time
colnames_split <- strsplit(colnames(logFC_matrix), "_")
drug <- sapply(colnames_split, function(x) x[1])
conc <- sapply(colnames_split, function(x) x[2])
time <- sapply(colnames_split, function(x) x[3])
# Create the desired column order: CX 0.1 3hr, CX 0.5 3hr, CX 0.1 24hr, CX 0.5 24hr, CX 0.1 48h, CX 0.5 48h,
# DOX 0.1 3hr, DOX 0.5 3hr, DOX 0.1 24hr, DOX 0.5 24hr, DOX 0.1 48h, DOX 0.5 48h
desired_order <- c("CX_0.1_3", "CX_0.5_3", "CX_0.1_24", "CX_0.5_24", "CX_0.1_48", "CX_0.5_48",
"DOX_0.1_3", "DOX_0.5_3", "DOX_0.1_24", "DOX_0.5_24", "DOX_0.1_48", "DOX_0.5_48")
# Reorder columns in the matrix based on the desired order
column_names <- paste(drug, conc, time, sep = "_")
column_order <- match(desired_order, column_names)
logFC_matrix <- logFC_matrix[, column_order]
signif_matrix <- signif_matrix[, column_order]
drug <- drug[column_order]
conc <- conc[column_order]
time <- time[column_order]
# Prepare annotations matching the column structure
ha_top <- HeatmapAnnotation(
Drug = drug,
Conc = conc,
Time = time,
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(2, 2, 2), "cm")
)
# Create the heatmap
heatmap <- Heatmap(logFC_matrix, name = "logFC", top_annotation = ha_top,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(signif_matrix[i, j], x, y, gp = gpar(fontsize = 10))
},
show_row_names = TRUE, show_column_names = FALSE,
column_title = "Genes in DOX cardiotoxicity-associated loci\nresponse to CX5461 and DOX",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
cluster_columns = FALSE) # Disable column clustering
# Draw the heatmap
draw(heatmap, heatmap_legend_side = "right", annotation_legend_side = "right")
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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] reshape2_1.4.4
[2] circlize_0.4.16
[3] ComplexHeatmap_2.18.0
[4] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
[5] RColorBrewer_1.1-3
[6] clusterProfiler_4.10.1
[7] pheatmap_1.0.12
[8] qvalue_2.34.0
[9] BiocParallel_1.36.0
[10] Homo.sapiens_1.3.1
[11] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[12] org.Hs.eg.db_3.18.0
[13] GO.db_3.18.0
[14] OrganismDbi_1.44.0
[15] GenomicFeatures_1.54.4
[16] GenomicRanges_1.54.1
[17] GenomeInfoDb_1.38.8
[18] AnnotationDbi_1.64.1
[19] IRanges_2.36.0
[20] S4Vectors_0.40.2
[21] Biobase_2.62.0
[22] BiocGenerics_0.48.1
[23] edgeR_4.0.16
[24] limma_3.58.1
[25] cluster_2.1.8.1
[26] ggfortify_0.4.17
[27] lubridate_1.9.4
[28] forcats_1.0.0
[29] stringr_1.5.1
[30] dplyr_1.1.4
[31] purrr_1.0.4
[32] readr_2.1.5
[33] tidyr_1.3.1
[34] tibble_3.2.1
[35] ggplot2_3.5.2
[36] tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] splines_4.3.0 later_1.3.2
[3] BiocIO_1.12.0 bitops_1.0-9
[5] ggplotify_0.1.2 filelock_1.0.3
[7] polyclip_1.10-7 graph_1.80.0
[9] XML_3.99-0.18 lifecycle_1.0.4
[11] doParallel_1.0.17 rprojroot_2.0.4
[13] lattice_0.22-7 MASS_7.3-60
[15] magrittr_2.0.3 sass_0.4.10
[17] rmarkdown_2.29 jquerylib_0.1.4
[19] yaml_2.3.10 httpuv_1.6.15
[21] cowplot_1.1.3 DBI_1.2.3
[23] abind_1.4-8 zlibbioc_1.48.2
[25] ggraph_2.2.1 RCurl_1.98-1.17
[27] yulab.utils_0.2.0 tweenr_2.0.3
[29] rappdirs_0.3.3 git2r_0.36.2
[31] GenomeInfoDbData_1.2.11 enrichplot_1.22.0
[33] ggrepel_0.9.6 tidytree_0.4.6
[35] codetools_0.2-20 DelayedArray_0.28.0
[37] DOSE_3.28.2 xml2_1.3.8
[39] ggforce_0.4.2 shape_1.4.6.1
[41] tidyselect_1.2.1 aplot_0.2.5
[43] farver_2.1.2 viridis_0.6.5
[45] matrixStats_1.5.0 BiocFileCache_2.10.2
[47] GenomicAlignments_1.38.2 jsonlite_2.0.0
[49] GetoptLong_1.0.5 tidygraph_1.3.1
[51] iterators_1.0.14 foreach_1.5.2
[53] tools_4.3.0 progress_1.2.3
[55] treeio_1.26.0 Rcpp_1.0.12
[57] glue_1.7.0 gridExtra_2.3
[59] SparseArray_1.2.4 xfun_0.52
[61] MatrixGenerics_1.14.0 withr_3.0.2
[63] BiocManager_1.30.25 fastmap_1.2.0
[65] digest_0.6.34 timechange_0.3.0
[67] R6_2.6.1 gridGraphics_0.5-1
[69] colorspace_2.1-0 Cairo_1.6-2
[71] biomaRt_2.58.2 RSQLite_2.3.9
[73] generics_0.1.3 data.table_1.17.0
[75] rtracklayer_1.62.0 prettyunits_1.2.0
[77] graphlayouts_1.2.2 httr_1.4.7
[79] S4Arrays_1.2.1 scatterpie_0.2.4
[81] whisker_0.4.1 pkgconfig_2.0.3
[83] gtable_0.3.6 blob_1.2.4
[85] workflowr_1.7.1 XVector_0.42.0
[87] shadowtext_0.1.4 htmltools_0.5.8.1
[89] fgsea_1.28.0 RBGL_1.78.0
[91] clue_0.3-66 scales_1.3.0
[93] png_0.1-8 ggfun_0.1.8
[95] knitr_1.50 rstudioapi_0.17.1
[97] tzdb_0.5.0 rjson_0.2.23
[99] nlme_3.1-168 curl_6.2.2
[101] GlobalOptions_0.1.2 cachem_1.1.0
[103] parallel_4.3.0 HDO.db_0.99.1
[105] restfulr_0.0.15 pillar_1.10.2
[107] vctrs_0.6.5 promises_1.3.2
[109] dbplyr_2.5.0 evaluate_1.0.3
[111] magick_2.8.6 cli_3.6.1
[113] locfit_1.5-9.12 compiler_4.3.0
[115] Rsamtools_2.18.0 rlang_1.1.3
[117] crayon_1.5.3 plyr_1.8.9
[119] fs_1.6.3 stringi_1.8.3
[121] viridisLite_0.4.2 munsell_0.5.1
[123] Biostrings_2.70.3 lazyeval_0.2.2
[125] GOSemSim_2.28.1 Matrix_1.6-1.1
[127] patchwork_1.3.0 hms_1.1.3
[129] bit64_4.6.0-1 KEGGREST_1.42.0
[131] statmod_1.5.0 SummarizedExperiment_1.32.0
[133] igraph_2.1.4 memoise_2.0.1
[135] bslib_0.9.0 ggtree_3.10.1
[137] fastmatch_1.1-6 bit_4.6.0
[139] gson_0.1.0 ape_5.8-1