Lipid_810
borangao
2023-10-09
Last updated: 2023-10-11
Checks: 7 0
Knit directory: meSuSie_Analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 1f54cb8 | borangao | 2023-10-11 | Update my analysis |
| html | 428bdf0 | borangao | 2023-10-09 | Build site. |
| Rmd | ba8db56 | borangao | 2023-10-09 | Update my analysis |
| html | 504f3a9 | borangao | 2023-10-09 | Build site. |
| Rmd | 62ce4b3 | borangao | 2023-10-09 | Update my analysis |
810 Regions of 4 lipid traits
Note: all the code and analysis reproduced here can be found in Repository
This analysis is done by relaxing the P-value threshold when constructing candidate regions.
Feature of 95% credible set
a. Set size and eQTL enrichment of 95% credible set
library(ggpubr)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
library(ggpmisc)
library(VennDiagram)
library(gridExtra)
library(ggbreak)
library(DescTools)
library(coin)
library(susieR)
library(ggrepel)
library(stringr)
load("/net/fantasia/home/borang/Susie_Mult/Revision_Round_1/01_07_Real_Data/summary_res/res.RData")
#####################################################################
#
# Plot directory and theme
#
####################################################################
plot_dir<-"/net/fantasia/home/borang/Susie_Mult/Revision_Round_1/Real_Data/Lipid_810/Figure/"
system(paste0("mkdir -p ",plot_dir))
custom_theme <- function() {
theme(
axis.text.x = element_text(size = 5),
axis.text.y = element_text(size = 5),
axis.title.x = element_text(size = 7, face="bold"),
axis.title.y = element_text(size = 7, face="bold"),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
strip.background = element_blank(),
legend.text = element_text(size=7),
legend.title = element_text(size=7, face="bold"),
plot.title = element_text(size=7, hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = "black")
)
}
################################################
#
# Set Size/Z-score/eQTL
#
#
###############################################
################################################
#
# Set SiZe Part
#
###############################################
###Median set size by Trait
all_sets_info<-data.frame(res_all%>%group_by(Trait,Region) %>% summarise(across(c("MESuSiE_cs", "SuSiE_cs","Paintor_cs"), ~ sum(.x, na.rm = TRUE))))%>%filter(MESuSiE_cs!=0, SuSiE_cs!=0, Paintor_cs!=0) ###Median Set Size across all locus
all_sets_info_long<-all_sets_info%>%pivot_longer(!(Trait|Region), names_to = "Method", values_to = "Count")
all_sets_info_long$Method<-factor(all_sets_info_long$Method,levels=c("MESuSiE_cs","SuSiE_cs","Paintor_cs"))
levels(all_sets_info_long$Method)<-c("MESuSiE","SuSiE","Paintor")
p_set = ggplot(data =all_sets_info_long,aes(x = Trait, y=Count,fill=Method))+geom_boxplot(aes(x = Trait,fill=Method),outlier.size = 0.1,fatten = 0.5,color = "darkgray")+scale_fill_manual(values=c("MESuSiE"="#023e8a","SuSiE"="#2a9d8f","Paintor"="#f4a261"),guide=FALSE)
p_set =p_set + theme_bw() + xlab("") +ylab("Set Size")+coord_cartesian(ylim=c(0,175))
p_set= p_set+custom_theme()
################################################
#
# Z-score Part
#
###############################################
MESuSiE_cs_Z<-res_all%>%group_by(Trait) %>%filter(MESuSiE_cs==1)%>%summarise(zmax = median(pmax(abs(zscore_WB),abs(zscore_BB))))
SuSiE_cs_Z<-res_all%>%group_by(Trait) %>%filter(SuSiE_cs==1)%>%summarise(zmax =median(pmax(abs(zscore_WB),abs(zscore_BB))))%>%pull(zmax)
Paintor_cs_Z<-res_all%>%group_by(Trait) %>%filter(Paintor_cs==1)%>%summarise(zmax = median(pmax(abs(zscore_WB),abs(zscore_BB))))%>%pull(zmax)
set_size_z_info<-data.frame(cbind(MESuSiE_cs_Z,SuSiE_cs_Z,Paintor_cs_Z))
colnames(set_size_z_info)<-c("Trait",c("MESuSiE","SuSiE","Paintor"))
set_size_z_info_long<-set_size_z_info %>%pivot_longer(!(Trait), names_to = "Method", values_to = "Z")%>%mutate(Method = factor(Method, levels=c("MESuSiE","SuSiE","Paintor")))
p_z = ggplot(data = set_size_z_info_long,aes(x = Trait, y=Z,fill=Method))+geom_bar( stat = "identity",position="dodge")+scale_fill_manual(values=c("MESuSiE"="#023e8a","SuSiE"="#2a9d8f","Paintor"="#f4a261"))
p_z = p_z + geom_text(label = round(set_size_z_info_long$Z,2),position = position_dodge(width = 1),vjust=-0.5,size = 5*5/14)
p_z = p_z + theme_bw() + xlab("") +ylab("Median |Z|")+ ylim(0,max(round(set_size_z_info_long$Z,2)+1))
p_z = p_z +custom_theme()
################################################
#
# eQTL enrichment
#
#
###############################################
ann_col_name<-c("missense", "synonymous", "utr_comb", "promotor", "CRE","liver_ind_eQTL")
# Functions for calculating fold enrichment
calc_fold_enrichment <- function(df, cs_col, ann_col_name) {
df %>%
group_by(Region) %>%
filter(sum(!!sym(cs_col)) != 0) %>%
group_by(Trait, !!sym(cs_col)) %>%
summarise(across(ann_col_name, ~ sum(.x, na.rm = TRUE) / n())) %>%
group_by(Trait) %>%
summarise(across(ann_col_name, ~ .x[!!sym(cs_col) == 1] / .x[!!sym(cs_col) == 0]))
}
res_all<-res_all%>%mutate(utr_comb = ifelse((utr_5+utr_3)!=0,1,0))
MESuSiE_PIP_ann <- calc_fold_enrichment(res_all, "MESuSiE_cs", ann_col_name)
SuSiE_PIP_ann <- calc_fold_enrichment(res_all, "SuSiE_cs", ann_col_name)
Paintor_PIP_ann <- calc_fold_enrichment(res_all, "Paintor_cs", ann_col_name)
# Combine results
Trait_CS_enrichment <- bind_rows(
MESuSiE_PIP_ann %>% mutate(Method = "MESuSiE"),
SuSiE_PIP_ann %>% mutate(Method = "SuSiE"),
Paintor_PIP_ann %>% mutate(Method = "Paintor")
) %>% mutate(Method = factor(Method, levels = c("MESuSiE", "SuSiE", "Paintor")))%>%
dplyr::select(Trait,liver_ind_eQTL ,Method )%>%dplyr::rename(eQTL = liver_ind_eQTL)
# Pivot to long format
Trait_CS_enrichment_long <- Trait_CS_enrichment %>%
pivot_longer(cols = -c(Method, Trait), names_to = "Cat", values_to = "Prop") %>%
mutate(Method = factor(Method, levels = c("MESuSiE", "SuSiE", "Paintor")))
p_eQTL <- ggplot(Trait_CS_enrichment_long, aes(x = Trait, y = Prop, fill = Method)) +
geom_bar(stat = "identity", position = "dodge") +scale_fill_manual(values = c("MESuSiE" = "#023e8a", "SuSiE" = "#2a9d8f", "Paintor" = "#f4a261")) +
geom_text(,label = round(Trait_CS_enrichment_long$Prop,2),position = position_dodge(width = 1),vjust=-0.5,size = 5*5/14)+
xlab("") + ylab("eQTL Fold Enrichment") + ylim(0,max(round(Trait_CS_enrichment_long$Prop))+1)+
theme_bw() + custom_theme()
p_out<-p_set/p_z/p_eQTL+plot_layout(guides = "collect",heights = c(1.5,1,1))+plot_annotation(tag_levels = 'a')&theme(legend.position = 'bottom',plot.tag = element_text(size = 7, face = "bold"))
p_out
| Version | Author | Date |
|---|---|---|
| 504f3a9 | borangao | 2023-10-09 |
b. Functional enrichment of 95% credible set and top signals
################################################################################
#
#
# Functional Annotation enrichment for 95% credible set SNPS
#
#
################################################################################
# Enrichment of 95% credible set without by trait
calc_fold_enrichment_marginal<-function(df, cs_col, ann_col_name) {
df %>%group_by(Region) %>%
filter(sum(!!sym(cs_col)) != 0) %>%
group_by( !!sym(cs_col)) %>%
summarise(across(ann_col_name, ~ sum(.x, na.rm = TRUE) / n())) %>%
summarise(across(ann_col_name, ~ .x[!!sym(cs_col) == 1] / .x[!!sym(cs_col) == 0]))
}
MESuSiE_PIP_ann <- calc_fold_enrichment_marginal(res_all, "MESuSiE_cs", ann_col_name)
SuSiE_PIP_ann <- calc_fold_enrichment_marginal(res_all, "SuSiE_cs", ann_col_name)
Paintor_PIP_ann <- calc_fold_enrichment_marginal(res_all, "Paintor_cs", ann_col_name)
CS_enrichment <- bind_rows(
MESuSiE_PIP_ann %>% mutate(Method = "MESuSiE"),
SuSiE_PIP_ann %>% mutate(Method = "SuSiE"),
Paintor_PIP_ann %>% mutate(Method = "Paintor")
) %>% mutate(Method = factor(Method, levels = c("MESuSiE", "SuSiE", "Paintor")))%>%
dplyr::rename(Missense = missense ,Synonymous = synonymous,UTR = utr_comb,Promotor = promotor,eQTL = liver_ind_eQTL)
# Pivot to long format
CS_enrichment_long <- CS_enrichment %>%
pivot_longer(cols = -c(Method), names_to = "Cat", values_to = "Prop") %>%
mutate(Method = factor(Method, levels = c("MESuSiE", "SuSiE", "Paintor"))) %>%
mutate(Cat = factor(Cat, levels = c("Missense", "Synonymous", "UTR", "Promotor", "CRE","eQTL")))%>%
mutate(Prop = round(Prop, 2))
p_set <- ggplot(data = CS_enrichment_long,aes(x = Cat, y = Prop, fill = Method)) +
geom_col(position = "dodge") + scale_fill_manual(values = c("MESuSiE" = "#023e8a", "SuSiE" = "#2a9d8f", "Paintor" = "#f4a261")) +
geom_text(aes(x=Cat,group=Method,y=Prop,label=Prop),position = position_dodge(width = 1),vjust=-0.5,size = 5/14*5) +
geom_hline(yintercept = 1, linetype = "dashed") +
xlab("") + ylab("Fold Enrichment Credible Set") +ylim(0,round(max(CS_enrichment_long$Prop))+1) +
theme_bw() + custom_theme()
################################################################################
#
#
# Functional Annotation enrichment for top 500 PIP SNPs
#
#
################################################################################
# Enrichment of top 500 signal
top_N_signal = 500
bg_an<-res_all%>%summarise(across(ann_col_name,~ sum(.x, na.rm = TRUE)/n()))
MESuSiE_Signal_ann<-res_all%>%filter(MESuSiE_PIP_Shared>0.5|MESuSiE_PIP_WB>0.5|MESuSiE_PIP_BB>0.5)%>% arrange(desc(MESuSiE_PIP_Either))%>%top_n(n = top_N_signal, wt = MESuSiE_PIP_Either)%>%summarise(across(ann_col_name,~ sum(.x, na.rm = TRUE)/n()))/bg_an
SuSiE_Signal_ann<-res_all%>%filter(SuSiE_PIP>0.5)%>% arrange(desc(SuSiE_PIP))%>%top_n(n = top_N_signal, wt = SuSiE_PIP)%>%summarise(across(ann_col_name,~ sum(.x, na.rm = TRUE)/n()))/bg_an
Paintor_Signal_ann<-res_all%>%filter(Paintor_PIP>0.5) %>% arrange(desc(Paintor_PIP))%>%top_n(n = top_N_signal, wt = Paintor_PIP)%>%summarise(across(ann_col_name,~ sum(.x, na.rm = TRUE)/n()))/bg_an
Signal_enrichment <- bind_rows(
MESuSiE_Signal_ann %>% mutate(Method = "MESuSiE"),
SuSiE_Signal_ann %>% mutate(Method = "SuSiE"),
Paintor_Signal_ann %>% mutate(Method = "Paintor")
) %>% mutate(Method = factor(Method, levels = c("MESuSiE", "SuSiE", "Paintor")))%>%
dplyr::rename(Missense = missense ,Synonymous = synonymous,UTR = utr_comb,Promotor = promotor,eQTL = liver_ind_eQTL)
# Pivot to long format
Signal_enrichment_long <- Signal_enrichment %>%
pivot_longer(cols = -c(Method), names_to = "Cat", values_to = "Prop") %>%
mutate(Method = factor(Method, levels = c("MESuSiE", "SuSiE", "Paintor"))) %>%
mutate(Cat = factor(Cat, levels = c("Missense", "Synonymous", "UTR", "Promotor", "CRE","eQTL")))%>%
mutate(Prop = round(Prop, 2))
p_signal <- ggplot(data = Signal_enrichment_long,aes(x = Cat, y = Prop, fill = Method)) +
geom_col(position = "dodge") + scale_fill_manual(values = c("MESuSiE" = "#023e8a", "SuSiE" = "#2a9d8f", "Paintor" = "#f4a261")) +
geom_text(aes(x=Cat,group=Method,y=Prop,label=Prop),position = position_dodge(width = 1),vjust=-0.5,size = 5/14*5) +
geom_hline(yintercept = 1, linetype = "dashed") +
xlab("") + ylab("Fold Enrichment Top Signal") +ylim(0,round(max(Signal_enrichment_long$Prop))+1) +
theme_bw() + custom_theme()
p_out<-p_set/p_signal+plot_annotation(tag_levels = 'a')&theme(legend.position = 'bottom',plot.tag = element_text(size = 7, face = "bold"))
p_out
| Version | Author | Date |
|---|---|---|
| 504f3a9 | borangao | 2023-10-09 |
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3; LAPACK version 3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: America/New_York
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] stringr_1.5.0 ggrepel_0.9.1 susieR_0.11.84
[4] coin_1.4-2 survival_3.3-1 DescTools_0.99.45
[7] ggbreak_0.1.1 gridExtra_2.3 VennDiagram_1.7.3
[10] futile.logger_1.4.3 ggpmisc_0.4.7 ggpp_0.4.4
[13] patchwork_1.1.1 tidyr_1.3.0 dplyr_1.1.2
[16] data.table_1.14.8 ggpubr_0.6.0 ggplot2_3.4.2
[19] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] formatR_1.14 gld_2.6.5 sandwich_3.0-2
[4] readxl_1.4.2 rlang_1.1.1 magrittr_2.0.3
[7] git2r_0.32.0 multcomp_1.4-25 matrixStats_1.0.0
[10] e1071_1.7-13 compiler_4.3.1 getPass_0.2-2
[13] callr_3.7.3 vctrs_0.6.2 quantreg_5.95
[16] crayon_1.5.2 pkgconfig_2.0.3 fastmap_1.1.1
[19] backports_1.4.1 labeling_0.4.2 utf8_1.2.3
[22] promises_1.2.0.1 rmarkdown_2.22 ps_1.7.2
[25] MatrixModels_0.5-1 purrr_1.0.1 xfun_0.39
[28] modeltools_0.2-23 cachem_1.0.8 aplot_0.1.10
[31] jsonlite_1.8.3 highr_0.10 later_1.3.1
[34] reshape_0.8.9 irlba_2.3.5.1 broom_1.0.5
[37] parallel_4.3.1 R6_2.5.1 bslib_0.5.0
[40] stringi_1.7.12 car_3.1-2 boot_1.3-28.1
[43] jquerylib_0.1.4 cellranger_1.1.0 Rcpp_1.0.11
[46] knitr_1.39 zoo_1.8-12 httpuv_1.6.11
[49] Matrix_1.5-4.1 splines_4.3.1 tidyselect_1.2.0
[52] rstudioapi_0.14 abind_1.4-5 yaml_2.3.7
[55] codetools_0.2-19 processx_3.8.0 plyr_1.8.8
[58] lattice_0.20-45 tibble_3.2.1 withr_2.5.1
[61] evaluate_0.18 gridGraphics_0.5-1 lambda.r_1.2.4
[64] proxy_0.4-27 pillar_1.9.0 carData_3.0-5
[67] whisker_0.4.1 stats4_4.3.1 ggfun_0.0.9
[70] generics_0.1.3 rprojroot_2.0.3 munsell_0.5.0
[73] scales_1.2.1 rootSolve_1.8.2.3 class_7.3-20
[76] glue_1.6.2 lmom_2.8 tools_4.3.1
[79] SparseM_1.81 ggsignif_0.6.4 Exact_3.1
[82] fs_1.6.2 mvtnorm_1.1-3 libcoin_1.0-9
[85] colorspace_2.1-0 cli_3.6.1 futile.options_1.0.1
[88] fansi_1.0.5 expm_0.999-7 mixsqp_0.3-48
[91] gtable_0.3.1 rstatix_0.7.2 yulab.utils_0.0.4
[94] sass_0.4.6 digest_0.6.30 TH.data_1.1-2
[97] ggplotify_0.1.0 farver_2.1.1 htmltools_0.5.5
[100] lifecycle_1.0.3 httr_1.4.6 MASS_7.3-57