Enrichment analysis for 6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al
XSun
2024-10-23
Last updated: 2024-10-24
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Knit directory: multigroup_ctwas_analysis/
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library(tidyr)
library(dplyr)
library(VennDiagram)
library(ggplot2)
traits <- c("LDL-ukb-d-30780_irnt","SBP-ukb-a-360","WBC-ieu-b-30","aFib-ebi-a-GCST006414","SCZ-ieu-b-5102","IBD-ebi-a-GCST004131")
dbs <- c("GO_Biological_Process_2023","GO_Cellular_Component_2023","GO_Molecular_Function_2023")
trait<- "LDL-ukb-d-30780_irnt"
db <- "GO_Biological_Process_2023"Methods
We do enrichment analysis for the genes with PIP > 0.8 here: https://sq-96.github.io/multigroup_ctwas_analysis/multi_group_6traits_15weights_ess.html
The gene set membership was downloaded here: https://maayanlab.cloud/Enrichr/#libraries
Background genes
For Fractional model and Fisher exact test, we selected 2 kind of backgroud genes
- All genes used in ctwas
- All genes in the selected geneset database.
For enrichR, the background genes are not modifiable. The background genes are all genes in the selected geneset database
Remove the redundant
Using EnrichR package.
This package was used in our earlier ctwas paper.
- It takes a list of genes(genes with PIP > 0.8) as input and returns the enriched GO terms with adjusted p-values.
Fractional model
The model is:
glm(PIP ~ gene set membership, family = quasibinomial('logit')).
We do this regression for one gene set at a time.
The PIP vector contains:
- all genes within the credible set: we use their actual PIPs
- genes without the credible set & PIP < 0.1: we set the PIPs
as
0.5*min(gene pip within credible set)
The 2 different baselines:
- All genes from ctwas. Here,
genes without the credible set & PIP < 0.1includes only the genes used in ctwas. - All genes from the geneset database. Here,
genes without the credible set & PIP < 0.1includes the union of all genes from the GO terms in the geneset database.
Fisher exact test
We assign 1 to the genes with PIP > 0.5/0.8 & in cs and 0 for others. We name this vector as binarized_PIP. We test the association between the binarized_PIP and geneset_membership.
The testing matrix is:
| geneset_membership | 0 | 1 |
|---|---|---|
| binarized_pip 0 | a | b |
| binarized_pip 1 | c | d |
Where:
ais the count wherebinarized_pip = 0andgeneset_membership = 0.bis the count wherebinarized_pip = 0andgeneset_membership = 1.cis the count wherebinarized_pip = 1andgeneset_membership = 0.dis the count wherebinarized_pip = 1andgeneset_membership = 1.
The 2 different baselines:
- All genes from ctwas. Here,
geneset_membershipmatrix includes only the genes used in ctwas. - All genes from the geneset database. Here,
geneset_membershipmatrix includes the union of all genes from the GO terms in the geneset database.
Comparing the p-values from Enrichr and Fisher exact test – baseline genes are all genes from gene sets
p_enrichr <- c()
p_fet <- c()
#compare_diff <- c()
for (trait in traits) {
for (db in dbs) {
file_enrichr <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/postprocess/enrichment_redundant_",trait,"_",db,".rdata")
if(file.exists(file_enrichr)) {
load(file_enrichr)
load(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/postprocess/enrichment_fisher_blgeneset_pip08_",trait,"_",db,".rdata"))
merged <- merge(db_enrichment, summary, by.x = "Term", by.y = "GO")
p_enrichr <- c(p_enrichr, merged$P.value)
p_fet <- c(p_fet, merged$pvalue)
#compare_diff <- rbind(compare_diff, merged)
}
}
}
p_enrichr <- as.numeric(p_enrichr)
p_fet <- as.numeric(p_fet)
df <- data.frame(p_enrichr = p_enrichr, p_fet = p_fet)
# Fit a linear model to calculate the slope
fit <- lm(p_fet ~ p_enrichr)
slope <- coef(fit)[2]
intercept <- coef(fit)[1]
ggplot(df, aes(x = p_enrichr, y = p_fet)) +
geom_point() + # Add points
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") + # y = x line
geom_smooth(method = "lm", se = FALSE, color = "blue") + # Best-fit line
annotate("text", x = max(p_enrichr) * 0.8, y = max(p_fet) * 0.9,
label = paste0("Slope: ", round(slope, 3)),
color = "blue") + # Slope text
annotate("text", x = max(p_enrichr) * 0.8, y = max(p_enrichr) * 0.8,
label = "y = x", color = "red", size = 5, hjust = -0.1, vjust = -0.5) + # y = x text near the line
ggtitle("Comparison of p-values between enrichr and FET, baseline -- all genes from gene sets") + # Add title
xlab("Enrichr p-values") + # x-axis label
ylab("FET p-values") + # y-axis label
theme_minimal()
Summary for the number of Go terms with p-values < 0.001
load("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/postprocess/enrichment_summary_for_all_redundant_p.rdata")
DT::datatable(summary,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Number of enriched GO terms under different settings'),options = list(pageLength = 20) )
Comparing the GO terms reported by FET and Fractional model – p-values < 0.001, baseline genes are genes used in ctwas, redundant terms NOT removed
pval <- 0.001
all_fractional <- c()
all_fet <- c()
for (trait in traits) {
for (db in dbs) {
file_fet <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/postprocess/enrichment_fisher_blctwas_pip08_",trait,"_",db,".rdata")
load(file_fet)
all_fet <- rbind(all_fet,summary[as.numeric(summary$pvalue) < pval,])
file_fractional <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/postprocess/enrichment_fractional_",trait,"_",db,".rdata")
load(file_fractional)
summary$trait <- trait
summary$db <- db
all_fractional <- rbind(all_fractional,summary[as.numeric(summary$pvalue) < pval,])
}
}
all_fractional <- all_fractional[complete.cases(all_fractional),]
all_fractional$id <- paste0(all_fractional$trait,"-",all_fractional$db,"-",all_fractional$GO)
all_fet$id <- paste0(all_fet$trait,"-",all_fet$db,"-",all_fet$GO)
venn.plot <- draw.pairwise.venn(
area1 = nrow(all_fractional), # Size of Group A
area2 = nrow(all_fet), # Size of Group B
cross.area = sum(all_fractional$id %in% all_fet$id), # Overlap between Group A and Group B
category = c("Fractional", "FET"), # Labels for the groups
fill = c("red", "blue"), # Colors for the groups
lty = "blank", # Line type for the circles
cex = 2, # Font size for the numbers
cat.cex = 2 # Font size for the labels
)
| Version | Author | Date |
|---|---|---|
| 2446887 | XSun | 2024-10-23 |
all_fractional <- all_fractional[,c("trait","db","GO","pvalue","fdr","id")]
DT::datatable(all_fractional[!all_fractional$id %in% all_fet$id,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Unique GO terms for fractional model'),options = list(pageLength = 10) ) DT::datatable(all_fet[!all_fet$id %in% all_fractional$id,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Unique GO terms for FET'),options = list(pageLength = 10) )Comparing the GO terms reported by FET and Fractional model – p-values < 0.001, baseline genes are all genes from gene sets, redundant terms NOT removed
pval <- 0.001
all_fractional <- c()
all_fet <- c()
for (trait in traits) {
for (db in dbs) {
file_fet <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/postprocess/enrichment_fisher_blgeneset_pip08_",trait,"_",db,".rdata")
load(file_fet)
all_fet <- rbind(all_fet,summary[as.numeric(summary$pvalue) < pval,])
file_fractional <- paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/postprocess/enrichment_fractional_blgeneset_",trait,"_",db,".rdata")
load(file_fractional)
summary$trait <- trait
summary$db <- db
all_fractional <- rbind(all_fractional,summary[as.numeric(summary$pvalue) < pval,])
}
}
all_fractional <- all_fractional[complete.cases(all_fractional),]
all_fractional$id <- paste0(all_fractional$trait,"-",all_fractional$db,"-",all_fractional$GO)
all_fet$id <- paste0(all_fet$trait,"-",all_fet$db,"-",all_fet$GO)
venn.plot <- draw.pairwise.venn(
area1 = nrow(all_fractional), # Size of Group A
area2 = nrow(all_fet), # Size of Group B
cross.area = sum(all_fractional$id %in% all_fet$id), # Overlap between Group A and Group B
category = c("Fractional", "FET"), # Labels for the groups
fill = c("red", "blue"), # Colors for the groups
lty = "blank", # Line type for the circles
cex = 2, # Font size for the numbers
cat.cex = 2 # Font size for the labels
)
| Version | Author | Date |
|---|---|---|
| 2446887 | XSun | 2024-10-23 |
all_fractional <- all_fractional[,c("trait","db","GO","pvalue","fdr","id")]
DT::datatable(all_fractional[!all_fractional$id %in% all_fet$id,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Unique GO terms for fractional model'),options = list(pageLength = 10) ) DT::datatable(all_fet[!all_fet$id %in% all_fractional$id,],caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Unique GO terms for FET'),options = list(pageLength = 10) )
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] C
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggplot2_3.5.1 VennDiagram_1.7.3 futile.logger_1.4.3
[4] dplyr_1.1.4 tidyr_1.3.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.41 bslib_0.3.1
[4] purrr_1.0.2 splines_4.2.0 lattice_0.20-45
[7] colorspace_2.0-3 vctrs_0.6.5 generics_0.1.2
[10] htmltools_0.5.2 yaml_2.3.5 mgcv_1.8-40
[13] utf8_1.2.2 rlang_1.1.2 jquerylib_0.1.4
[16] later_1.3.0 pillar_1.9.0 glue_1.6.2
[19] withr_2.5.0 lambda.r_1.2.4 lifecycle_1.0.4
[22] stringr_1.5.1 munsell_0.5.0 gtable_0.3.0
[25] workflowr_1.7.0 htmlwidgets_1.5.4 evaluate_0.15
[28] labeling_0.4.2 knitr_1.39 fastmap_1.1.0
[31] crosstalk_1.2.0 httpuv_1.6.5 fansi_1.0.3
[34] highr_0.9 Rcpp_1.0.12 DT_0.22
[37] promises_1.2.0.1 scales_1.3.0 formatR_1.12
[40] jsonlite_1.8.0 farver_2.1.0 fs_1.5.2
[43] digest_0.6.29 stringi_1.7.6 rprojroot_2.0.3
[46] cli_3.6.1 tools_4.2.0 magrittr_2.0.3
[49] sass_0.4.1 tibble_3.2.1 futile.options_1.0.1
[52] whisker_0.4 pkgconfig_2.0.3 Matrix_1.5-3
[55] rmarkdown_2.25 rstudioapi_0.13 R6_2.5.1
[58] nlme_3.1-157 git2r_0.30.1 compiler_4.2.0