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We compared overlap with gene lists from chatgpt, across three
analyses: coloc, TWAS, and cTWAS. The prompt we used in
chatgpt 4o is
can you return some genes with roles in LDL / LDL risk genes, in txt table format, the second column is the gene functions
DT::datatable(matrix())traits <- c("LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131","T1D-GCST90014023","aFib-ebi-a-GCST006414","SBP-ukb-a-360","BMI-panukb","HB-panukb","Height-panukb")
#
folder_results <- "/project/xinhe/xsun/multi_group_ctwas/23.multi_group_0515/results_downstream/enrichr_compare/"
ctwas_folder_results <- "/project/xinhe/xsun/multi_group_ctwas/23.multi_group_0515/snakemake_outputs/"
folder_chatgpt_genes <- "/project/xinhe/xsun/multi_group_ctwas/23.multi_group_0515/data/genelist/"
all_overlap_tables <- list()
for (trait in traits){
# print(trait)
# Load gene lists
ctwas_genes <- readRDS(paste0(ctwas_folder_results, trait, "/", trait, ".3qtls.combined_pip_rmmapping_bygroup_final.RDS"))
ctwas_genes_pip05p <- ctwas_genes$gene_name[ctwas_genes$combined_pip >= 0.5]
ctwas_genes_pip05m <- ctwas_genes$gene_name[ctwas_genes$combined_pip < 0.5]
ctwas_genes_pip08p <- ctwas_genes$gene_name[ctwas_genes$combined_pip >= 0.8]
ctwas_genes_pip08m <- ctwas_genes$gene_name[ctwas_genes$combined_pip < 0.8]
ctwas_genes <- ctwas_genes$gene_name[ctwas_genes$combined_pip > 0.8]
coloc_genes <- readRDS(paste0(folder_results, trait, ".genes.coloc.RDS"))
coloc_genes <- coloc_genes$gene_name[coloc_genes$PP4 > 0.8]
twas_genes <- readRDS(paste0(folder_results, trait, ".genes.twas_bonf.RDS"))
chatgpt_genes <- read.table(paste0(folder_chatgpt_genes, trait, "_genelist_chatgpt.txt"), header = TRUE, sep = "\t")
# Calculate overlaps
overlap_counts <- c(
length(intersect(ctwas_genes, chatgpt_genes$Gene)),
length(intersect(coloc_genes, chatgpt_genes$Gene)),
length(intersect(twas_genes, chatgpt_genes$Gene))
)
percent_overlap <- c(
paste0(round(overlap_counts[1] / length(ctwas_genes) * 100, 1), "%"),
paste0(round(overlap_counts[2] / length(coloc_genes) * 100, 1), "%"),
paste0(round(overlap_counts[3] / length(twas_genes) * 100, 1), "%")
)
overlapping_genes <- c(
paste(intersect(ctwas_genes, chatgpt_genes$Gene), collapse = ", "),
paste(intersect(coloc_genes, chatgpt_genes$Gene), collapse = ", "),
paste(intersect(twas_genes, chatgpt_genes$Gene), collapse = ", ")
)
overlap_table <- data.frame(
Trait = trait,
Analysis = c("cTWAS", "COLOC", "TWAS"),
Total_Genes = c(length(ctwas_genes), length(coloc_genes), length(twas_genes)),
Overlap_with_ChatGPT = c(
paste0(length(intersect(ctwas_genes, chatgpt_genes$Gene))," of ",length(chatgpt_genes$Gene)," Chatgpt genes"),
paste0(length(intersect(coloc_genes, chatgpt_genes$Gene))," of ",length(chatgpt_genes$Gene)," Chatgpt genes"),
paste0(length(intersect(twas_genes, chatgpt_genes$Gene))," of ",length(chatgpt_genes$Gene)," Chatgpt genes")
),
Percent_Overlap = c(
paste0(round(length(intersect(ctwas_genes, chatgpt_genes$Gene)) / length(ctwas_genes) * 100, 1), "%"),
paste0(round(length(intersect(coloc_genes, chatgpt_genes$Gene)) / length(coloc_genes) * 100, 1), "%"),
paste0(round(length(intersect(twas_genes, chatgpt_genes$Gene)) / length(twas_genes) * 100, 1), "%")
),
Overlapping_Genes = c(
paste(intersect(ctwas_genes, chatgpt_genes$Gene), collapse = ", "),
paste(intersect(coloc_genes, chatgpt_genes$Gene), collapse = ", "),
paste(intersect(twas_genes, chatgpt_genes$Gene), collapse = ", ")
)
)
num_gene05p_in_set <- sum(ctwas_genes_pip05p %in% chatgpt_genes$Gene)
num_gene05m_in_set <- sum(ctwas_genes_pip05m %in% chatgpt_genes$Gene)
fisher_b_matrix_05p05m <- matrix(
c(num_gene05p_in_set, num_gene05m_in_set,
length(ctwas_genes_pip05p) - num_gene05p_in_set,
length(ctwas_genes_pip05m) - num_gene05m_in_set),
nrow = 2, byrow = TRUE,
dimnames = list(c("#included", "#notincluded"), c("pip>0.5", "pip<0.5"))
)
fisher_b_result_05p05m <- fisher.test(fisher_b_matrix_05p05m)
overlap_table$`fisher_p-PIP>0.5&PIP<0.5` <- NA
overlap_table$`fisher_p-PIP>0.5&PIP<0.5`[1] <- round(fisher_b_result_05p05m$p.value,digits = 8)
num_gene08p_in_set <- sum(ctwas_genes_pip08p %in% chatgpt_genes$Gene)
num_gene08m_in_set <- sum(ctwas_genes_pip08m %in% chatgpt_genes$Gene)
fisher_b_matrix_08p08m <- matrix(
c(num_gene08p_in_set, num_gene08m_in_set,
length(ctwas_genes_pip08p) - num_gene08p_in_set,
length(ctwas_genes_pip08m) - num_gene08m_in_set),
nrow = 2, byrow = TRUE,
dimnames = list(c("#included", "#notincluded"), c("pip>0.8", "pip<0.8"))
)
fisher_b_result_08p08m <- fisher.test(fisher_b_matrix_08p08m)
overlap_table$`fisher_p-PIP>0.8&PIP<0.8` <- NA
overlap_table$`fisher_p-PIP>0.8&PIP<0.8`[1] <- round(fisher_b_result_08p08m$p.value,digits = 8)
all_overlap_tables[[trait]] <- overlap_table
}
# Combine all into one data frame
combined_overlap_table <- do.call(rbind, all_overlap_tables)
rownames(combined_overlap_table) <- NULL
cat("<br><br>")cat(knitr::knit_print(DT::datatable(combined_overlap_table,
caption = htmltools::tags$caption(
style = 'caption-side: left; text-align: left; color:black; font-size:150% ;',
'Combined Gene Overlap Summary Across Traits'
),
options = list(pageLength = 10))))cat("<br><br>")### print the genes from chatgpt
for (trait in traits){
print(trait)
# Load gene lists
ctwas_genes <- readRDS(paste0(ctwas_folder_results, trait, "/", trait, ".3qtls.combined_pip_rmmapping_bygroup_final.RDS"))
ctwas_genes <- ctwas_genes$gene_name[ctwas_genes$combined_pip > 0.8]
coloc_genes <- readRDS(paste0(folder_results, trait, ".genes.coloc.RDS"))
coloc_genes <- coloc_genes$gene_name[coloc_genes$PP4 > 0.8]
twas_genes <- readRDS(paste0(folder_results, trait, ".genes.twas_bonf.RDS"))
chatgpt_genes <- read.table(paste0(folder_chatgpt_genes, trait, "_genelist_chatgpt.txt"), header = TRUE, sep = "\t")
cat("<br>")
cat("<br>")
cat(knitr::knit_print(DT::datatable(chatgpt_genes,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black; font-size:150% ;',paste0('Genes returned by chatgpt - ',trait)),options = list(pageLength = 5) )))
cat("<br>")
cat("<br>")
# Calculate overlaps
overlap_counts <- c(
length(intersect(ctwas_genes, chatgpt_genes$Gene)),
length(intersect(coloc_genes, chatgpt_genes$Gene)),
length(intersect(twas_genes, chatgpt_genes$Gene))
)
percent_overlap <- c(
paste0(round(overlap_counts[1] / length(ctwas_genes) * 100, 1), "%"),
paste0(round(overlap_counts[2] / length(coloc_genes) * 100, 1), "%"),
paste0(round(overlap_counts[3] / length(twas_genes) * 100, 1), "%")
)
overlapping_genes <- c(
paste(intersect(ctwas_genes, chatgpt_genes$Gene), collapse = ", "),
paste(intersect(coloc_genes, chatgpt_genes$Gene), collapse = ", "),
paste(intersect(twas_genes, chatgpt_genes$Gene), collapse = ", ")
)
overlap_table <- data.frame(
Trait = trait,
Analysis = c("cTWAS", "COLOC", "TWAS"),
Total_Genes = c(length(ctwas_genes), length(coloc_genes), length(twas_genes)),
Overlap_with_ChatGPT = c(
paste0(length(intersect(ctwas_genes, chatgpt_genes$Gene))," of ",length(chatgpt_genes$Gene)," Chatgpt genes"),
paste0(length(intersect(coloc_genes, chatgpt_genes$Gene))," of ",length(chatgpt_genes$Gene)," Chatgpt genes"),
paste0(length(intersect(twas_genes, chatgpt_genes$Gene))," of ",length(chatgpt_genes$Gene)," Chatgpt genes")
),
Percent_Overlap = c(
paste0(round(length(intersect(ctwas_genes, chatgpt_genes$Gene)) / length(ctwas_genes) * 100, 1), "%"),
paste0(round(length(intersect(coloc_genes, chatgpt_genes$Gene)) / length(coloc_genes) * 100, 1), "%"),
paste0(round(length(intersect(twas_genes, chatgpt_genes$Gene)) / length(twas_genes) * 100, 1), "%")
),
Overlapping_Genes = c(
paste(intersect(ctwas_genes, chatgpt_genes$Gene), collapse = ", "),
paste(intersect(coloc_genes, chatgpt_genes$Gene), collapse = ", "),
paste(intersect(twas_genes, chatgpt_genes$Gene), collapse = ", ")
)
)
}[1] “LDL-ukb-d-30780_irnt”
[1] “IBD-ebi-a-GCST004131”
[1] “T1D-GCST90014023”
[1] “aFib-ebi-a-GCST006414”
[1] “SBP-ukb-a-360”
[1] “BMI-panukb”
[1] “HB-panukb”
[1] “Height-panukb”