Genes in AC toxicity-associated loci response to CX5461 and DOX
Last updated: 2025-07-03
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📌 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
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,
23262, 150383, 9620, 79730, 344595, 5066, 6251, 3482, 9588, 339416,
7292, 55157, 87769, 23409, 720, 3107, 54535, 1590, 80059, 7991,
57110, 8803, 323, 54826, 5916, 23371, 283337, 64078, 80010, 1933,
10818, 51020
)
# Load DEG data
load_deg <- function(name) {
read.csv(paste0("data/DEGs/Toptable_", name, ".csv"))
}
samples <- 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")
deg_list <- lapply(samples, load_deg)
names(deg_list) <- samples
# Subset and annotate DEG tables
get_subset <- function(df, name) {
parts <- strsplit(name, "_")[[1]]
df %>%
filter(Entrez_ID %in% Entrez_IDs) %>%
dplyr::select(Entrez_ID, logFC, adj.P.Val) %>%
mutate(Drug = parts[1], Conc = parts[2], Time = parts[3])
}
combined_data <- bind_rows(mapply(get_subset, deg_list, names(deg_list), SIMPLIFY = FALSE))
# Add Gene symbol and significance
combined_data <- combined_data %>%
mutate(Gene = mapIds(org.Hs.eg.db, keys = as.character(Entrez_ID),
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Significance = ifelse(adj.P.Val < 0.05, "*", ""))📌 rsID + Study annotation
📌Create a matrix and heatmap
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")
)
# Row annotation
rsid_factor <- factor(rsid_info$rsID)
study_factor <- factor(rsid_info$Study, levels = c("GWAS1", "GWAS2", "TWAS"))
rsid_colors <- setNames(rainbow(length(levels(rsid_factor))), levels(rsid_factor))
study_colors <- setNames(RColorBrewer::brewer.pal(length(levels(study_factor)), "Set2"), levels(study_factor))
ha_left <- rowAnnotation(
rsID = anno_text(rsid_info$rsID, location = 0, just = "left", gp = gpar(fontsize = 9)),
rsID_color = rsid_factor,
Study = study_factor,
col = list(
rsID_color = rsid_colors,
Study = study_colors
),
show_legend = c(rsID = FALSE, rsID_color = FALSE, Study = TRUE),
annotation_name_side = "top",
annotation_width = unit(c(4.3, 0.25, 0.25), "cm")
)
# Draw final heatmap
Heatmap(logFC_matrix,
name = "logFC",
top_annotation = ha_top,
left_annotation = ha_left,
cluster_columns = FALSE,
cluster_rows = FALSE,
show_row_names = TRUE,
show_column_names = FALSE,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(signif_matrix[i, j], x, y, gp = gpar(fontsize = 9))
},
column_title = "AC Toxicity-Associated Genes: CX-5461 vs DOX",
column_title_gp = gpar(fontsize = 14, fontface = "bold")
)
📌 DOX Cardiotoxicity
# Load necessary libraries
library(tidyverse)
library(ComplexHeatmap)
library(circlize)
library(org.Hs.eg.db)
library(reshape2)
library(grid)
# Set Entrez ID order
Entrez_IDs <- c(847, 873, 2064, 2878, 2944, 3038, 4846, 51196, 5880, 6687,
7799, 4292, 5916, 3077, 51310, 9154, 64078, 5244, 10057, 10060,
89845, 56853, 4625, 1573, 79890)
# Load annotated gene table
annotated_genes <- read.csv("data/Annotated_DOX_Gene_Table.csv")
# Load DEG data
CX_0.1_3 <- read.csv("data/DEGs/Toptable_CX_0.1_3.csv")
CX_0.1_24 <- read.csv("data/DEGs/Toptable_CX_0.1_24.csv")
CX_0.1_48 <- read.csv("data/DEGs/Toptable_CX_0.1_48.csv")
CX_0.5_3 <- read.csv("data/DEGs/Toptable_CX_0.5_3.csv")
CX_0.5_24 <- read.csv("data/DEGs/Toptable_CX_0.5_24.csv")
CX_0.5_48 <- read.csv("data/DEGs/Toptable_CX_0.5_48.csv")
DOX_0.1_3 <- read.csv("data/DEGs/Toptable_DOX_0.1_3.csv")
DOX_0.1_24 <- read.csv("data/DEGs/Toptable_DOX_0.1_24.csv")
DOX_0.1_48 <- read.csv("data/DEGs/Toptable_DOX_0.1_48.csv")
DOX_0.5_3 <- read.csv("data/DEGs/Toptable_DOX_0.5_3.csv")
DOX_0.5_24 <- read.csv("data/DEGs/Toptable_DOX_0.5_24.csv")
DOX_0.5_48 <- read.csv("data/DEGs/Toptable_DOX_0.5_48.csv")
# Subsetting helper
get_subset <- function(df) {
df[df$Entrez_ID %in% Entrez_IDs, c("Entrez_ID", "logFC", "adj.P.Val")]
}
# Subset and annotate
add_metadata <- function(data, drug, conc, time) {
data %>% mutate(Drug = drug, Conc = conc, Time = time)
}
combined_data <- bind_rows(
add_metadata(get_subset(CX_0.1_3), "CX", 0.1, 3),
add_metadata(get_subset(CX_0.1_24), "CX", 0.1, 24),
add_metadata(get_subset(CX_0.1_48), "CX", 0.1, 48),
add_metadata(get_subset(CX_0.5_3), "CX", 0.5, 3),
add_metadata(get_subset(CX_0.5_24), "CX", 0.5, 24),
add_metadata(get_subset(CX_0.5_48), "CX", 0.5, 48),
add_metadata(get_subset(DOX_0.1_3), "DOX", 0.1, 3),
add_metadata(get_subset(DOX_0.1_24), "DOX", 0.1, 24),
add_metadata(get_subset(DOX_0.1_48), "DOX", 0.1, 48),
add_metadata(get_subset(DOX_0.5_3), "DOX", 0.5, 3),
add_metadata(get_subset(DOX_0.5_24), "DOX", 0.5, 24),
add_metadata(get_subset(DOX_0.5_48), "DOX", 0.5, 48)
)
# Add gene symbol and significance
combined_data <- combined_data %>%
mutate(Gene = mapIds(org.Hs.eg.db, keys = as.character(Entrez_ID),
column = "SYMBOL", keytype = "ENTREZID", multiVals = "first"),
Significance = ifelse(adj.P.Val < 0.05, "*", ""))
# Merge with mechanistic category
combined_data <- left_join(combined_data, annotated_genes, by = c("Entrez_ID" = "ENTREZID"))
# Reorder by Entrez_ID
combined_data$Entrez_ID <- factor(combined_data$Entrez_ID, levels = Entrez_IDs)
# Preserve correct gene order
ordered_genes <- combined_data %>%
distinct(Entrez_ID, Gene) %>%
arrange(factor(Entrez_ID, levels = Entrez_IDs)) %>%
pull(Gene)
# Create logFC and significance matrices
logFC_matrix <- acast(combined_data, Gene ~ paste(Drug, Conc, Time, sep = "_"), value.var = "logFC")
logFC_matrix <- logFC_matrix[ordered_genes, ]
signif_matrix <- acast(combined_data, Gene ~ paste(Drug, Conc, Time, sep = "_"), value.var = "Significance")
signif_matrix <- signif_matrix[ordered_genes, ]
# Desired column order: group by drug → conc → time
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_matrix <- logFC_matrix[, desired_order]
signif_matrix <- signif_matrix[, desired_order]
# Column metadata
split_cols <- strsplit(colnames(logFC_matrix), "_")
drug <- sapply(split_cols, function(x) x[1])
conc <- sapply(split_cols, function(x) x[2])
time <- sapply(split_cols, function(x) x[3])
# Top annotation
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")
)
# Mechanistic category row annotation
category_mapping <- combined_data %>%
distinct(Gene, Mechanistic_Implication) %>%
filter(Gene %in% ordered_genes) %>%
arrange(match(Gene, ordered_genes))
category_colors <- structure(
c("darkorange", "steelblue", "darkgreen", "firebrick", "gold", "mediumpurple", "cyan"),
names = unique(category_mapping$Mechanistic_Implication)
)
ha_left <- rowAnnotation(
Category = category_mapping$Mechanistic_Implication,
col = list(Category = category_colors),
show_annotation_name = TRUE,
annotation_name_side = "top"
)
# Draw heatmap
Heatmap(logFC_matrix,
name = "logFC",
top_annotation = ha_top,
left_annotation = ha_left,
show_row_names = TRUE,
row_names_gp = gpar(fontsize = 10),
show_column_names = FALSE,
cluster_rows = FALSE,
cluster_columns = FALSE,
column_title = "Genes in DOX cardiotoxicity-associated loci\nresponse to CX5461 and DOX",
column_title_gp = gpar(fontsize = 16, fontface = "bold"),
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(signif_matrix[i, j], x, y, gp = gpar(fontsize = 10))
}
)
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
[37] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.3.0 later_1.3.2
[3] BiocIO_1.12.0 ggplotify_0.1.2
[5] bitops_1.0-9 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] processx_3.8.6 lattice_0.22-7
[15] MASS_7.3-60 magrittr_2.0.3
[17] sass_0.4.10 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10
[21] httpuv_1.6.15 cowplot_1.1.3
[23] DBI_1.2.3 abind_1.4-8
[25] zlibbioc_1.48.2 ggraph_2.2.1
[27] RCurl_1.98-1.17 yulab.utils_0.2.0
[29] tweenr_2.0.3 rappdirs_0.3.3
[31] git2r_0.36.2 GenomeInfoDbData_1.2.11
[33] enrichplot_1.22.0 ggrepel_0.9.6
[35] tidytree_0.4.6 codetools_0.2-20
[37] DelayedArray_0.28.0 DOSE_3.28.2
[39] xml2_1.3.8 ggforce_0.4.2
[41] shape_1.4.6.1 tidyselect_1.2.1
[43] aplot_0.2.5 farver_2.1.2
[45] viridis_0.6.5 matrixStats_1.5.0
[47] BiocFileCache_2.10.2 GenomicAlignments_1.38.2
[49] jsonlite_2.0.0 GetoptLong_1.0.5
[51] tidygraph_1.3.1 iterators_1.0.14
[53] foreach_1.5.2 tools_4.3.0
[55] progress_1.2.3 treeio_1.26.0
[57] Rcpp_1.0.12 glue_1.7.0
[59] gridExtra_2.3 SparseArray_1.2.4
[61] xfun_0.52 MatrixGenerics_1.14.0
[63] withr_3.0.2 BiocManager_1.30.25
[65] fastmap_1.2.0 callr_3.7.6
[67] digest_0.6.34 gridGraphics_0.5-1
[69] timechange_0.3.0 R6_2.6.1
[71] colorspace_2.1-0 Cairo_1.6-2
[73] biomaRt_2.58.2 RSQLite_2.3.9
[75] generics_0.1.3 data.table_1.17.0
[77] rtracklayer_1.62.0 prettyunits_1.2.0
[79] graphlayouts_1.2.2 httr_1.4.7
[81] S4Arrays_1.2.1 scatterpie_0.2.4
[83] whisker_0.4.1 pkgconfig_2.0.3
[85] gtable_0.3.6 blob_1.2.4
[87] XVector_0.42.0 shadowtext_0.1.4
[89] htmltools_0.5.8.1 fgsea_1.28.0
[91] RBGL_1.78.0 clue_0.3-66
[93] scales_1.3.0 png_0.1-8
[95] ggfun_0.1.8 knitr_1.50
[97] rstudioapi_0.17.1 tzdb_0.5.0
[99] rjson_0.2.23 nlme_3.1-168
[101] curl_6.2.2 GlobalOptions_0.1.2
[103] cachem_1.1.0 parallel_4.3.0
[105] HDO.db_0.99.1 restfulr_0.0.15
[107] pillar_1.10.2 vctrs_0.6.5
[109] promises_1.3.2 dbplyr_2.5.0
[111] evaluate_1.0.3 magick_2.8.6
[113] cli_3.6.1 locfit_1.5-9.12
[115] compiler_4.3.0 Rsamtools_2.18.0
[117] rlang_1.1.3 crayon_1.5.3
[119] ps_1.8.1 getPass_0.2-4
[121] plyr_1.8.9 fs_1.6.3
[123] stringi_1.8.3 viridisLite_0.4.2
[125] munsell_0.5.1 Biostrings_2.70.3
[127] lazyeval_0.2.2 GOSemSim_2.28.1
[129] Matrix_1.6-1.1 patchwork_1.3.0
[131] hms_1.1.3 bit64_4.6.0-1
[133] KEGGREST_1.42.0 statmod_1.5.0
[135] SummarizedExperiment_1.32.0 igraph_2.1.4
[137] memoise_2.0.1 bslib_0.9.0
[139] ggtree_3.10.1 fastmatch_1.1-6
[141] bit_4.6.0 gson_0.1.0
[143] ape_5.8-1