RNA_CHDGene_Comp_Species
2026-01-28
Last updated: 2026-01-28
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Knit directory: CrossSpecies_CM_Diff_RNA/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 3686961 | John D. Hurley | 2026-01-28 | publish change |
| Rmd | 582a67d | John D. Hurley | 2026-01-28 | Code block headers added |
| Rmd | 5a82fc9 | John D. Hurley | 2026-01-28 | CHDGene website |
| Rmd | 085c1db | John D. Hurley | 2026-01-28 | Finalizing CorHeatMap |
#Loading Libraries
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(biomaRt)
library(tidyverse)
library(readxl)
library(readr)
library(reshape2)
library(circlize)
library(grid)
library(stringr)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(gprofiler2)
library(purrr)
# install.packages("biomartr")
#Loading R Objects
# quadrant_gene_lists <- readRDS("data/DGE/quadrant_gene_lists_DayBefore.RDS")
# chdgene_table <- readRDS("data/DGE/chdgene_table.RDS")
Day0_DEGs_HC <- readRDS("data/DGE/Species/Day0_DEGs_HC.RDS")
Day2_DEGs_HC <- readRDS("data/DGE/Species/Day2_DEGs_HC.RDS")
Day5_DEGs_HC <- readRDS("data/DGE/Species/Day5_DEGs_HC.RDS")
Day15_DEGs_HC <- readRDS("data/DGE/Species/Day15_DEGs_HC.RDS")
Day30_DEGs_HC <- readRDS("data/DGE/Species/Day30_DEGs_HC.RDS")
all_intersections <- readRDS("data/DGE/Species/all_intersections.RDS")generate_volcano_plot <- function(toptable, title) {
# #check for entrezid
# if(!"Entrez_ID" %in% colnames(toptable)) stop("Entrez_ID col not present")
#
#make significance labels
toptable <- toptable %>%
mutate(Significance = case_when(
logFC > 0 & adj.P.Val < 0.05 ~ "Upregulated",
logFC < 0 & adj.P.Val < 0.05 ~ "Downregulated",
TRUE ~ "Not Significant"
))
#factor significance
toptable$Significance <- factor(
toptable$Significance,
levels = c("Upregulated",
"Not Significant",
"Downregulated")
)
#count genes in each category
upgenes <- sum(toptable$Significance == "Upregulated")
nsgenes <- sum(toptable$Significance == "Not Significant")
downgenes <- sum(toptable$Significance == "Downregulated")
#labels for legend
legend_lab <- c(
paste0("Upregulated: ", upgenes),
paste0("Not Significant: ", nsgenes),
paste0("Downregulated: ", downgenes)
)
#colors
color_map <- c("Upregulated" = "blue",
"Not Significant" = "grey30",
"Downregulated" = "red")
#generate volcano plots
p <- ggplot(toptable, aes(x = logFC,
y = -log10(P.Value),
color = Significance)) +
geom_point_rast(alpha = 0.8, size = 3) +
scale_color_manual(values = color_map,
labels = legend_lab,
breaks = c("Upregulated",
"Not Significant",
"Downregulated")) +
xlim(-20,20) +
labs(title = title,
x = expression("log"[2]*"FC"),
y = expression("-log"[10]*"p-value")) +
# theme_custom() +
theme(legend.position = "right")
return(p)
}generate_volcano_plot_CHDGene <- function(toptable, title, chdgene_genes = NULL) {
# Determine significance
toptable <- toptable %>%
mutate(Significance = case_when(
logFC > 0 & adj.P.Val < 0.05 ~ "Upregulated",
logFC < 0 & adj.P.Val < 0.05 ~ "Downregulated",
TRUE ~ "Not Significant"
))
toptable$Significance <- factor(
toptable$Significance,
levels = c("Upregulated", "Not Significant", "Downregulated")
)
# Mark GWAS hits
toptable <- toptable %>%
mutate(highlight = if_else(Gene %in% chdgene_genes, TRUE, FALSE))
# Split data for layers
df_non_chdgene <- toptable %>% filter(!highlight)
df_chdgene <- toptable %>% filter(highlight)
# Colors for significance
color_map <- c(
"Upregulated" = "blue",
"Not Significant" = "grey30",
"Downregulated" = "red"
)
p <- ggplot() +
# Non-chdgene points
geom_point(data = df_non_chdgene,
aes(x = logFC, y = -log10(P.Value), color = Significance),
alpha = 0.8, size = 3) +
scale_color_manual(values = color_map) +
# GWAS points on top with outline
geom_point(data = df_chdgene,
aes(x = logFC, y = -log10(P.Value), fill = Significance),
shape = 21, color = "limegreen", size = 4, stroke = 1.5) +
# Optional: label chdgene genes
# geom_text_repel(data = df_gwas,
# aes(x = logFC, y = -log10(P.Value), label = Gene),
# size = 3, fontface = "bold", color = "black", max.overlaps = 20) +
xlim(-20, 20) +
labs(title = title,
x = expression("log"[2]*"FC"),
y = expression("-log"[10]*"p-value")) +
theme_bw() +
theme(legend.position = "right") +
guides(color = guide_legend(override.aes = list(size = 3)),
fill = guide_legend(override.aes = list(size = 3)))
return(p)
}chdgene_table <- read.csv("C:/Users/jdhurley/Downloads/chdgene_table.csv")
saveRDS(chdgene_table,"data/DGE/chdgene_table.RDS")
# Make a copy to be safe
chdgene_table_copy <- chdgene_table
# Check
colnames(chdgene_table_copy)[1] "Gene" "CHD.classification"
[3] "Extra.cardiac.phenotype" "Inheritance.mode"
[5] "Ranking" "Supporting.References" # Should include "Gene"
# dim(chdgene_table_copy)
# length(unique(chdgene_table_copy$Gene))
# unique(chdgene_table_copy$Gene)
# Connect to Ensembl
ensembl <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
# Your Day0-only DEGs (Ensembl IDs)
day0_only <- all_intersections$Day0
# Map Ensembl IDs to gene symbols
mapping <- getBM(
attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = day0_only,
mart = ensembl
)
# Remove empty mappings
mapping <- mapping[mapping$hgnc_symbol != "", ]
# Filter by your known gene list
known_genes <- chdgene_table_copy$Gene
day0_filtered <- mapping$ensembl_gene_id[mapping$hgnc_symbol %in% known_genes]
# Optional: get gene symbols for filtered genes
day0_filtered_symbols <- mapping$hgnc_symbol[mapping$hgnc_symbol %in% known_genes]
saveRDS(day0_filtered_symbols,"data/DGE/Species/Day0_Only_DEGs_CHDGenes.RDS")
# Your day2-only DEGs (Ensembl IDs)
day2_only <- all_intersections$Day2
# Map Ensembl IDs to gene symbols
mapping <- getBM(
attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = day2_only,
mart = ensembl
)
# Remove empty mappings
mapping <- mapping[mapping$hgnc_symbol != "", ]
# Filter by your known gene list
known_genes <- chdgene_table_copy$Gene
day2_filtered <- mapping$ensembl_gene_id[mapping$hgnc_symbol %in% known_genes]
# Optional: get gene symbols for filtered genes
day2_filtered_symbols <- mapping$hgnc_symbol[mapping$hgnc_symbol %in% known_genes]
saveRDS(day2_filtered_symbols,"data/DGE/Species/Day2_Only_DEGs_CHDGenes.RDS")
# Your day2-only DEGs (Ensembl IDs)
day5_only <- all_intersections$Day5
# Map Ensembl IDs to gene symbols
mapping <- getBM(
attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = day5_only,
mart = ensembl
)
# Remove empty mappings
mapping <- mapping[mapping$hgnc_symbol != "", ]
# Filter by your known gene list
known_genes <- chdgene_table_copy$Gene
day5_filtered <- mapping$ensembl_gene_id[mapping$hgnc_symbol %in% known_genes]
# Optional: get gene symbols for filtered genes
day5_filtered_symbols <- mapping$hgnc_symbol[mapping$hgnc_symbol %in% known_genes]
saveRDS(day5_filtered_symbols,"data/DGE/Species/day5_Only_DEGs_CHDGenes.RDS")
# Your day2-only DEGs (Ensembl IDs)
day15_only <- all_intersections$Day15
# Map Ensembl IDs to gene symbols
mapping <- getBM(
attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = day15_only,
mart = ensembl
)
# Remove empty mappings
mapping <- mapping[mapping$hgnc_symbol != "", ]
# Filter by your known gene list
known_genes <- chdgene_table_copy$Gene
day15_filtered <- mapping$ensembl_gene_id[mapping$hgnc_symbol %in% known_genes]
# Optional: get gene symbols for filtered genes
day15_filtered_symbols <- mapping$hgnc_symbol[mapping$hgnc_symbol %in% known_genes]
saveRDS(day15_filtered_symbols,"data/DGE/Species/day15_Only_DEGs_CHDGenes.RDS")
# Your day2-only DEGs (Ensembl IDs)
day30_only <- all_intersections$Day30
# Map Ensembl IDs to gene symbols
mapping <- getBM(
attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = day30_only,
mart = ensembl
)
# Remove empty mappings
mapping <- mapping[mapping$hgnc_symbol != "", ]
# Filter by your known gene list
known_genes <- chdgene_table_copy$Gene
day30_filtered <- mapping$ensembl_gene_id[mapping$hgnc_symbol %in% known_genes]
# Optional: get gene symbols for filtered genes
day30_filtered_symbols <- mapping$hgnc_symbol[mapping$hgnc_symbol %in% known_genes]
saveRDS(day30_filtered_symbols,"data/DGE/Species/day30_Only_DEGs_CHDGenes.RDS")
# Your day2-only DEGs (Ensembl IDs)
AllDays_only <- all_intersections$`Day0&Day2&Day5&Day15&Day30`
# Map Ensembl IDs to gene symbols
mapping <- getBM(
attributes = c("ensembl_gene_id", "hgnc_symbol"),
filters = "ensembl_gene_id",
values = AllDays_only,
mart = ensembl
)
# Remove empty mappings
mapping <- mapping[mapping$hgnc_symbol != "", ]
# Filter by your known gene list
known_genes <- chdgene_table_copy$Gene
AllDays_filtered <- mapping$ensembl_gene_id[mapping$hgnc_symbol %in% known_genes]
# Optional: get gene symbols for filtered genes
AllDays_filtered_symbols <- mapping$hgnc_symbol[mapping$hgnc_symbol %in% known_genes]
saveRDS(AllDays_filtered_symbols,"data/DGE/Species/AllDays_Only_DEGs_CHDGenes.RDS") # Create the table
Day0_Only_DEGs_ChiSq <- matrix(c(16, 1719, 8, 1184), nrow = 2, byrow = TRUE)
rownames(Day0_Only_DEGs_ChiSq) <- c("Day0", "Day0-2-5-15-30")
colnames(Day0_Only_DEGs_ChiSq) <- c("CHDGene", "Non-CHDGene")
# Run chi-square test
chisq.test(Day0_Only_DEGs_ChiSq)
Pearson's Chi-squared test with Yates' continuity correction
data: Day0_Only_DEGs_ChiSq
X-squared = 0.2824, df = 1, p-value = 0.5951# Create the table
Day2_Only_DEGs_ChiSq <- matrix(c(6, 441, 8, 1184), nrow = 2, byrow = TRUE)
rownames(Day2_Only_DEGs_ChiSq) <- c("Day2", "Day0-2-5-15-30")
colnames(Day2_Only_DEGs_ChiSq) <- c("CHDGene", "Non-CHDGene")
# Run chi-square test
chisq.test(Day2_Only_DEGs_ChiSq)
Pearson's Chi-squared test with Yates' continuity correction
data: Day2_Only_DEGs_ChiSq
X-squared = 1.0274, df = 1, p-value = 0.3108# Create the table
Day5_Only_DEGs_ChiSq <- matrix(c(6,499, 8, 1184), nrow = 2, byrow = TRUE)
rownames(Day5_Only_DEGs_ChiSq) <- c("Day5", "Day0-2-5-15-30")
colnames(Day5_Only_DEGs_ChiSq) <- c("CHDGene", "Non-CHDGene")
# Run chi-square test
chisq.test(Day5_Only_DEGs_ChiSq)
Pearson's Chi-squared test with Yates' continuity correction
data: Day5_Only_DEGs_ChiSq
X-squared = 0.613, df = 1, p-value = 0.4337# Create the table
Day15_Only_DEGs_ChiSq <- matrix(c(14,602, 8, 1184), nrow = 2, byrow = TRUE)
rownames(Day15_Only_DEGs_ChiSq) <- c("Day15", "Day0-2-5-15-30")
colnames(Day15_Only_DEGs_ChiSq) <- c("CHDGene", "Non-CHDGene")
# Run chi-square test
chisq.test(Day15_Only_DEGs_ChiSq)
Pearson's Chi-squared test with Yates' continuity correction
data: Day15_Only_DEGs_ChiSq
X-squared = 7.3855, df = 1, p-value = 0.006575# Create the table
Day30_Only_DEGs_ChiSq <- matrix(c(18,1217, 8, 1184), nrow = 2, byrow = TRUE)
rownames(Day30_Only_DEGs_ChiSq) <- c("Day30", "Day0-2-5-15-30")
colnames(Day30_Only_DEGs_ChiSq) <- c("CHDGene", "Non-CHDGene")
# Run chi-square test
chisq.test(Day30_Only_DEGs_ChiSq)
Pearson's Chi-squared test with Yates' continuity correction
data: Day30_Only_DEGs_ChiSq
X-squared = 2.8359, df = 1, p-value = 0.09218df <- data.frame(
Day = c("Day0", "Day2", "Day5", "Day15", "Day30","Shared_All"),
CHDGene = c(16,6,6,14,18,8),
Non_CHDGene = c(1719,441,499,602,1217,1884)
)
# Convert to long format for ggplot
df_long <- df %>%
pivot_longer(cols = c(CHDGene, Non_CHDGene),
names_to = "Type",
values_to = "Count") %>%
group_by(Day) %>%
mutate(Proportion = Count / sum(Count)) # calculate proportions
df_long$Day <- factor(df_long$Day, levels = c("Day0", "Day2", "Day5", "Day15", "Day30","Shared_All"), ordered = TRUE)
ggplot(df_long, aes(x = Day, y = Proportion, fill = Type)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels = scales::percent_format()) + # show % on y-axis
scale_fill_manual(values = c("DEGs" = "#E41A1C", "CHDGene" = "#377EB8")) + # custom colors
labs(y = "Proportion", x = "Day", fill = "") +
theme_minimal(base_size = 14)
# git -> commit all changes
# git -> push
# wflow_publish("analysis/RNA_CHDGene_Comp_Species.Rmd")
sessionInfo()R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
LAPACK version 3.12.1
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] gprofiler2_0.2.4 org.Hs.eg.db_3.21.0 AnnotationDbi_1.70.0
[4] IRanges_2.42.0 S4Vectors_0.46.0 Biobase_2.68.0
[7] BiocGenerics_0.54.0 generics_0.1.4 circlize_0.4.17
[10] reshape2_1.4.5 readxl_1.4.5 lubridate_1.9.4
[13] forcats_1.0.1 purrr_1.1.0 readr_2.1.5
[16] tibble_3.3.0 tidyverse_2.0.0 biomaRt_2.64.0
[19] ggplot2_4.0.0 stringr_1.5.2 tidyr_1.3.1
[22] dplyr_1.1.4 workflowr_1.7.2
loaded via a namespace (and not attached):
[1] DBI_1.2.3 httr2_1.2.2 rlang_1.1.6
[4] magrittr_2.0.3 git2r_0.36.2 otel_0.2.0
[7] compiler_4.5.1 RSQLite_2.4.3 getPass_0.2-4
[10] png_0.1-8 callr_3.7.6 vctrs_0.6.5
[13] pkgconfig_2.0.3 shape_1.4.6.1 crayon_1.5.3
[16] fastmap_1.2.0 dbplyr_2.5.1 XVector_0.48.0
[19] labeling_0.4.3 promises_1.3.3 rmarkdown_2.30
[22] tzdb_0.5.0 UCSC.utils_1.4.0 ps_1.9.1
[25] bit_4.6.0 xfun_0.53 cachem_1.1.0
[28] GenomeInfoDb_1.44.3 jsonlite_2.0.0 progress_1.2.3
[31] blob_1.3.0 later_1.4.4 prettyunits_1.2.0
[34] R6_2.6.1 bslib_0.9.0 stringi_1.8.7
[37] RColorBrewer_1.1-3 jquerylib_0.1.4 cellranger_1.1.0
[40] Rcpp_1.1.0 knitr_1.51 httpuv_1.6.16
[43] timechange_0.3.0 tidyselect_1.2.1 rstudioapi_0.18.0
[46] yaml_2.3.10 curl_7.0.0 processx_3.8.6
[49] plyr_1.8.9 withr_3.0.2 KEGGREST_1.48.1
[52] S7_0.2.0 evaluate_1.0.5 BiocFileCache_2.16.2
[55] xml2_1.5.1 Biostrings_2.76.0 pillar_1.11.1
[58] filelock_1.0.3 whisker_0.4.1 plotly_4.11.0
[61] rprojroot_2.1.1 hms_1.1.4 scales_1.4.0
[64] glue_1.8.0 lazyeval_0.2.2 tools_4.5.1
[67] data.table_1.18.0 fs_1.6.6 colorspace_2.1-2
[70] GenomeInfoDbData_1.2.14 cli_3.6.5 rappdirs_0.3.4
[73] viridisLite_0.4.2 gtable_0.3.6 sass_0.4.10
[76] digest_0.6.37 htmlwidgets_1.6.4 farver_2.1.2
[79] memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.5
[82] httr_1.4.7 GlobalOptions_0.1.3 bit64_4.6.0-1