Lipid 711
borangao
2023-10-09
Last updated: 2023-10-09
Checks: 7 0
Knit directory: meSuSie_Analysis/
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711 Regions of 4 lipid traits
Note: all the code and analysis reproduced here can be found in Zenodo
1. Feature of 95% credible set
1a. 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_06_Real_Data/summary_res/res.RData")
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$Z,2)+1))
p_z = p_z +custom_theme()
################################################
#
# eQTL enrichment
#
#
###############################################
ann_col_name<-c("missense", "synonymous", "utr_comb", "promotor", "CRE","liver_ind_eQTL")
# Functions for calculating fold enrichment
calc_fold_enrichment <- function(df, cs_col, ann_col_name) {
df %>%
group_by(Region) %>%
filter(sum(!!sym(cs_col)) != 0) %>%
group_by(Trait, !!sym(cs_col)) %>%
summarise(across(ann_col_name, ~ sum(.x, na.rm = TRUE) / n())) %>%
group_by(Trait) %>%
summarise(across(ann_col_name, ~ .x[!!sym(cs_col) == 1] / .x[!!sym(cs_col) == 0]))
}
MESuSiE_PIP_ann <- calc_fold_enrichment(res_all, "MESuSiE_cs", ann_col_name)
SuSiE_PIP_ann <- calc_fold_enrichment(res_all, "SuSiE_cs", ann_col_name)
Paintor_PIP_ann <- calc_fold_enrichment(res_all, "Paintor_cs", ann_col_name)
# Combine results
Trait_CS_enrichment <- bind_rows(
MESuSiE_PIP_ann %>% mutate(Method = "MESuSiE"),
SuSiE_PIP_ann %>% mutate(Method = "SuSiE"),
Paintor_PIP_ann %>% mutate(Method = "Paintor")
) %>% mutate(Method = factor(Method, levels = c("MESuSiE", "SuSiE", "Paintor")))%>%
dplyr::select(Trait,liver_ind_eQTL ,Method )%>%dplyr::rename(eQTL = liver_ind_eQTL)
# Pivot to long format
Trait_CS_enrichment_long <- Trait_CS_enrichment %>%
pivot_longer(cols = -c(Method, Trait), names_to = "Cat", values_to = "Prop") %>%
mutate(Method = factor(Method, levels = c("MESuSiE", "SuSiE", "Paintor")))
p_eQTL <- ggplot(Trait_CS_enrichment_long, aes(x = Trait, y = Prop, fill = Method)) +
geom_bar(stat = "identity", position = "dodge") +scale_fill_manual(values = c("MESuSiE" = "#023e8a", "SuSiE" = "#2a9d8f", "Paintor" = "#f4a261")) +
geom_text(,label = round(Trait_CS_enrichment_long$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_annotation(tag_levels = 'a')+plot_layout(guides = "collect",heights = c(1.5,1,1))&theme(legend.position = 'bottom',plot.tag = element_text(size = 7,face="bold"))
p_out
1b. 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
res_all<-res_all%>%mutate(Paintor_Signal = ifelse(Paintor_PIP>0.5,1,0))
res_all<-res_all%>%mutate(SuSiE_Signal = case_when(
SuSiE_WB>0.5&SuSiE_BB>0.5~1,
SuSiE_WB>0.5&SuSiE_BB<0.5~2,
SuSiE_WB<0.5&SuSiE_BB>0.5~3,
.default =0))
res_all<-res_all%>%mutate(MESuSiE_Signal = case_when(
MESuSiE_PIP_Shared>0.5~1,
MESuSiE_PIP_WB>0.5~2,
MESuSiE_PIP_BB>0.5~3,
.default =0))
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_Signal!=0)%>% 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_Signal!=0)%>% 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_Signal!=0) %>% 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')+plot_layout(guides = "collect",heights = c(1,1))&theme(legend.position = 'bottom',plot.tag = element_text(size = 7,face="bold"))
p_out
2. Top signals with PIP > 0.5
2a. Proportion of shared and ancestry-specific signal
############################################################################
#
#
# Proportion of Signal Plot
#
#
############################################################################
Signal_number<- res_all%>%group_by(Trait)%>%
summarise(Paintor_Either_n = sum(Paintor_Signal!=0),
SuSiE_Shared_n = sum(SuSiE_Signal==1),
SuSiE_WB_n = sum(SuSiE_Signal==2),
SuSiE_BB_n = sum(SuSiE_Signal==3),
MESuSiE_Shared_n = sum(MESuSiE_Signal==1),
MESuSiE_WB_n = sum(MESuSiE_Signal==2),
MESuSiE_BB_n = sum(MESuSiE_Signal==3))
Signal_number<-Signal_number%>%
pivot_longer(cols = -c(Trait), names_to = "Cat", values_to = "Num")%>%
separate(Cat, into = c("Method", "Signal"), sep = "_", extra = "merge") %>%
mutate(
Method = case_when(
str_detect(Method, "MESuSiE") ~ "MESuSiE",
str_detect(Method, "SuSiE") ~ "SuSiE",
str_detect(Method, "Paintor") ~ "Paintor",
TRUE ~ Method
),
Signal = case_when(
str_detect(Signal, "BB_n") ~ "AFR",
str_detect(Signal, "WB_n") ~ "EUR",
str_detect(Signal, "Shared_n") ~ "Shared",
str_detect(Signal, "Either") ~ "Either",
TRUE ~ Signal
)
)
Signal_number<-Signal_number%>%group_by(Method,Trait)%>%mutate(prop = Num/sum(Num)*100,ypos = cumsum(prop)- 0.5*prop)%>%mutate(label = paste0(Signal," ",Num))
Signal_number<-Signal_number%>%mutate(Method = factor(Method, levels = c("MESuSiE","SuSiE","Paintor")),Signal = factor(Signal, levels = c("EUR","AFR","Shared","Either")))
signal_num_plot<-ggplot(Signal_number, aes(x="", y=prop, fill=Signal)) +
geom_bar(stat="identity", width=1, color="white") +
coord_polar("y", start=0) +
theme_void() +
theme(legend.position="none") +
geom_text(aes(y = ypos, label = label), size=7/14*5,color="white") +
scale_fill_manual(values=c("#ABDB9F","#F2C1B6","#6162B0","gray"))+
facet_grid(vars(Trait),vars(Method),labeller=label_parsed)+theme(
strip.text.x = element_text(size = 7,face="bold"),
strip.text.y = element_text(size = 7,face="bold"),
strip.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
signal_num_plot
2b. Feature of shared and ancestry-specific signal
###########################################################################################
#
#
# Correlation of ancestry-specific signal by MESuSiE and SuSiE
#
#
#########################################################################################
cor_ancestry_MESuSiE <- res_all %>%
filter(MESuSiE_Signal%in%c(2,3)) %>% filter(!(SuSiE_Signal%in%c(2,3)))%>%
mutate(Method = "MESuSiE") %>%
dplyr::select(Beta_WB, Beta_BB, Trait, Method)
cor_ancestry_SuSiE <- res_all %>%
filter(SuSiE_Signal%in%c(2,3)) %>%filter(!(MESuSiE_Signal%in%c(2,3)))%>%
mutate(Method = "SuSiE") %>%
dplyr::select(Beta_WB, Beta_BB, Trait, Method)
cor_ancestry_both <- res_all %>%filter(MESuSiE_Signal%in%c(2,3),SuSiE_Signal%in%c(2,3))%>%
mutate(Method = "Both") %>%
dplyr::select(Beta_WB, Beta_BB, Trait, Method)
cor_ancestry <- rbind(cor_ancestry_MESuSiE, cor_ancestry_SuSiE,cor_ancestry_both)
cor_ancestry<-cor_ancestry%>%mutate(Method = factor(Method,levels = c("MESuSiE","SuSiE","Both")))
betaplot<- ggscatter(cor_ancestry, x = "Beta_WB", y = "Beta_BB",
add = "reg.line", # Add regressin line
add.params = list(color = "red", fill = "lightgray",size = 0.5), # Customize reg. line
conf.int = FALSE, # Add confidence interval
size = 0.5
)+ stat_cor(method = "pearson",aes(label = ..r.label..),size = 5/14*5)+xlab("UKBB")+ylab("GLGC")+
theme_bw() + custom_theme()+
facet_wrap(vars(Method),ncol=3)
betaplot
#################################################################
#
#
# MAF and Conservative Score Part
#
#
##################################################################
################################################
#
# Phylop/GERP Score Part
#
#
################################################
res_all<- res_all %>%
mutate(
MESuSiE_Conservative = case_when(
MESuSiE_PIP_WB>0.5|MESuSiE_PIP_BB>0.5 ~ "Ancestry-specific",
MESuSiE_PIP_Shared>0.5 ~ "Shared",
TRUE ~ "None"
),
SuSiE_Conservative = case_when(
(SuSiE_WB>0.5&SuSiE_BB<0.5)|(SuSiE_BB>0.5&SuSiE_WB<0.5) ~ "Ancestry-specific",
SuSiE_Shared>0.5 ~ "Shared",
TRUE ~ "None"
)
)%>%
mutate(
MESuSiE_Conservative = factor(MESuSiE_Conservative, levels = c("None", "Ancestry-specific", "Shared")),
SuSiE_Conservative = factor(SuSiE_Conservative, levels = c("None", "Ancestry-specific", "Shared"))
)
####Phylop
grouped_data_phylop <- res_all %>%
mutate(abs_phylop = abs(phylop)) %>%
select(MESuSiE_Conservative, abs_phylop)%>%
group_by(MESuSiE_Conservative) %>%
summarise(phylop_values = list(abs_phylop))%>%
pull(phylop_values)
grouped_data_GERP <- res_all %>%
mutate(GERP = abs(GERP)) %>%
select(MESuSiE_Conservative, GERP)%>%
group_by(MESuSiE_Conservative) %>%
summarise(GERP_values = list(GERP))%>%
pull(GERP_values)
#########Conservation Plot
conserve_data<-res_all%>%mutate(Signal =MESuSiE_Conservative, Phylop =abs(phylop) )%>%select(Signal,Phylop,GERP)
conserve_data_long<-conserve_data%>%pivot_longer(!(Signal), names_to = "Method", values_to = "Score")
conserve_data_long$Method<-factor(conserve_data_long$Method,levels = c("Phylop","GERP"))
df <-conserve_data_long %>%group_by(Method,Signal) %>%summarize(ymin = min(Score,na.rm = T), lower = quantile(Score, .25,na.rm = T), middle = mean(Score,na.rm = T), upper = quantile(Score, .75,na.rm = T),ymax = max(Score,na.rm = T))
p_set_phylop = df %>% ggplot(aes(x = factor(Method), fill=Signal)) + geom_boxplot(fatten = 2,aes(ymin = ymin, ymax = ymax, lower = lower, upper = upper, middle = middle), stat = 'identity')+scale_fill_manual(values=c("None"="#DAFFED","Ancestry-specific"="#fffbdb","Shared" = "#7776bc"))
p_set_phylop =p_set_phylop + theme_bw() + xlab("") +ylab("Conservation Score")
p_set_phylop= p_set_phylop + custom_theme()
p_set_phylop= p_set_phylop+ theme(legend.position="bottom")
################################################
#
# MAF Part
#
#
################################################
maf_shared<-data.frame(res_all)%>%filter(MESuSiE_Signal==1)%>%mutate(MAF_diff = ifelse(MA_WB==MA_BB,MAF_WB-MAF_BB,1-MAF_WB-MAF_BB))%>%pull(MAF_diff)
maf_ancestry<-data.frame(res_all)%>%filter(MESuSiE_Signal%in%c(2,3))%>%mutate(MAF_diff = ifelse(MA_WB==MA_BB,MAF_WB-MAF_BB,1-MAF_WB-MAF_BB))%>%pull(MAF_diff)
maf_data_plot<-data.frame("MAF" = c(maf_shared,maf_ancestry),"Signal"=c(rep("Shared",length(maf_shared)),rep("Ancestry-specific",length(maf_ancestry))))
maf_data_plot$Signal<-factor(maf_data_plot$Signal)
maf_data_plot$Signal<-factor(maf_data_plot$Signal,levels = c(levels(maf_data_plot$Signal),"None"))
p_set = ggplot(data =maf_data_plot,aes(x = factor(Signal), y=MAF,fill=Signal))+geom_boxplot(aes(x = factor(Signal),fill=Signal),outlier.size = 0.01) +scale_fill_manual(values=c("None"="#DAFFED","Ancestry-specific"="#fffbdb","Shared" = "#7776bc"),guide=FALSE)
p_set = p_set + stat_compare_means(size= 5/14*5)
p_set =p_set + theme_bw() + xlab("") +ylab("MAF difference")
p_set= p_set + custom_theme()
p_set= p_set + theme(legend.position="none")
p_out<-p_set+p_set_phylop+plot_annotation(tag_levels = 'a') + plot_layout(guides = "collect") & theme(legend.position = "bottom",plot.tag = element_text(size = 7,face="bold"))
p_out
3. Example locus
3a. FAD2 Example
##############################################################
#
#
# Real Data Example Plotter
#
#
################################################################
gwas_plot_fun <- function(data_plot, xlab_name, ylab_name, yintercept) {
p_manhattan = ggplot() + geom_point(data = data_plot%>%filter(Lead_SNP==0), aes(x = POS, y = PIP, color = r2), size = 1)
p_manhattan = p_manhattan + geom_point(data = data_plot%>%filter(Lead_SNP==1), aes(x = POS, y = PIP), size = 1.5, color = "red") +
geom_text_repel(data = data_plot%>%filter(Lead_SNP==1), mapping = aes(x = POS, y = PIP, label = SNP), vjust = 1.2, size = 7/14*5, show.legend =FALSE)
p_manhattan = p_manhattan +
scale_color_stepsn(
colors = c("navy", "lightskyblue", "green", "orange", "red"),
breaks = seq(0.2, 0.8, by = 0.2),
limits = c(0, 1),
show.limits = TRUE,
na.value = 'grey50',
name = expression(R^2)
)
p_manhattan = p_manhattan +
geom_hline(
yintercept = yintercept,
linetype = "dashed",
color = "grey50",
size = 0.5
)
p_manhattan = p_manhattan +
geom_vline(
xintercept = data_plot%>%filter(lead_SNP==1)%>%pull(POS),
linetype = "dashed",
color = "grey50",
size = 0.5
)
p_manhattan = p_manhattan + xlim(min(data_plot$POS),max(data_plot$POS))
p_manhattan = p_manhattan + expand_limits(x = round(max(data_plot$POS)/1e6)*1e6)
if(max(data_plot$POS>1e6)){
p_manhattan = p_manhattan + scale_x_continuous(labels = function(x) paste0(x / 1e6, " MB"))
}
if(max(data_plot$POS<1e6)){
p_manhattan = p_manhattan + scale_x_continuous(labels = function(x) paste0(x / 1e3, " KB"))
}
p_manhattan = p_manhattan + xlab(xlab_name) +ylab(ylab_name)
p_manhattan = p_manhattan + guides(fill = guide_legend(title = as.expression(bquote(R^2))))
p_manhattan = p_manhattan + theme_bw()+custom_theme()
return(p_manhattan)
}
###Function used for PIP plot
finemap_plot_fun<-function(data_plot,xlab_name,ylab_name,yintercept){
p_manhattan = ggplot() + geom_point(data = data_plot, aes(x = POS, y = PIP, color = r2,shape = cat))+scale_shape_manual(name="Category",drop=FALSE,values=c(20,24,25,23,22))
p_manhattan = p_manhattan + geom_text_repel(data =data_plot%>%filter(Lead_SNP==1), mapping=aes(x=POS, y=PIP, label=SNP),vjust=1.2, size= 7/14*5,show.legend = FALSE)
p_manhattan = p_manhattan + theme_bw()+scale_color_stepsn(
colors = c("navy", "lightskyblue", "green", "orange", "red"),
breaks = seq(0.2, 0.8, by = 0.2),
limits = c(0, 1),
show.limits = TRUE,
na.value = 'grey50',
name = expression(R^2)
)
p_manhattan = p_manhattan + geom_hline(
yintercept =yintercept,
linetype = "dashed",
color = "grey50",
size = 0.5
) + geom_vline(
xintercept = data_plot%>%filter(lead_SNP==1)%>%pull(POS),
linetype = "dashed",
color = "grey50",
size = 0.5
)
p_manhattan = p_manhattan + xlim(min(data_plot$POS),max(data_plot$POS))
p_manhattan = p_manhattan + expand_limits(x = round(max(data_plot$POS)/1e6)*1e6)
if(max(data_plot$POS>1e6)){
p_manhattan = p_manhattan + scale_x_continuous(labels = function(x) paste0(x / 1e6, " MB"))
}
if(max(data_plot$POS<1e6)){
p_manhattan = p_manhattan + scale_x_continuous(labels = function(x) paste0(x / 1e3, " KB"))
}
p_manhattan= p_manhattan+xlab(xlab_name)+ylab(ylab_name)
p_manhattan= p_manhattan+guides(fill=guide_legend(title=as.expression(bquote(R^2))))
p_manhattan = p_manhattan + theme_bw()+custom_theme()
return(p_manhattan)
}
# Function used for gene plot
gene_range_plot_fun<-function(gene_list_data,plot.range){
p<-ggplot(data = gene_list_data) +
geom_linerange(aes(x = Gene, ymin = Start, ymax = End))+ylim(plot.range)+ expand_limits(y = round(max(plot.range[2])/1e6)*1e6)+scale_y_continuous(labels = function(y) paste0(y / 1e6, " MB"))+coord_flip()+
geom_text(aes(x = Gene, y = Start, label = Gene), hjust = "right", size = 5/14*5) + ylab(paste0("chr",unique(gsub("chr","",gene_list_data$Chrom))))+ xlab("Gene") +
theme_bw() + theme(
axis.text.x = element_text(size = 5),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(size = 7, face="bold"),
axis.title.y = element_text(size = 7, face="bold"),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
strip.background = element_blank(),
legend.text = element_text(size=7),
legend.title = element_text(size=7, face="bold"),
plot.title = element_text(size=7, hjust = 0.5),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.line.x = element_line(color = "black"),
axis.line.y = element_line(color = "black")
)
return(p)
}
#####################################################################################################################
#
#
#
# Example showcase
#
#
#
#####################################################################################################################
Gene_List<-fread("/net/fantasia/home/borang/Susie_Mult/simulation/simu_0120/data/Gencode_GRCh37_Genes_UniqueList2021.txt",header=T)
###################################################################
#
#
# FADS2 independent eQTL shared signal
#
#
##################################################################
region = 384
trait_name = "TG"
ref_panel = "UKB1"
p_threshold_index = 1
res_z_dir<-paste0("/net/fantasia/home/borang/Susie_Mult/Revision_Round_1/01_02_Real_Data/",trait_name,"/",trait_name,"_REF_",ref_panel,"_P",p_threshold_index,"/")
out_dir<-paste0(res_z_dir,"data/")
out_res_dir<-paste0(res_z_dir,"res/")
##Data Reprocess
WB_COV<-as.matrix(fread(paste0(out_dir,"Region_",region,".LD1")))
BB_COV<-as.matrix(fread(paste0(out_dir,"Region_",region,".LD2")))
load(paste0(out_res_dir,"MESuSiE_region_",region,".RData"))
candidate_region<-res_all%>%filter(Region==region)
# rs174564 is the eQTL of FADS2 gene, highlighted in the paper
lead_SNP = "rs174564"
lead_SNP_index<-which(candidate_region$SNP==lead_SNP)
candidate_region<-candidate_region%>%mutate(r2_EUR = unname(unlist((WB_COV[,lead_SNP_index])^2)) ,r2_AFR = unname(unlist((BB_COV[,lead_SNP_index])^2)),POS = as.numeric(POS))
####Category Setting
candidate_region<-candidate_region%>%mutate(SuSiE_cat = case_when(SuSiE_WB>0.5&SuSiE_BB>0.5 ~ 3,
SuSiE_WB>0.5&SuSiE_BB<0.5 ~ 1,
SuSiE_WB<0.5&SuSiE_BB>0.5 ~ 2,
TRUE ~ 0),
Paintor_cat = case_when(Paintor_PIP>0.5~4,
TRUE~0),
MESuSiE_cat = case_when(MESuSiE_PIP_WB>0.5~1,
MESuSiE_PIP_BB>0.5~2,
MESuSiE_PIP_Shared>0.5~3,
TRUE~0))
###GWAS PLOT
EUR_GWAS_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = -log10(2*pnorm(-abs(zscore_WB))),Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS))%>%select(SNP,POS, r2,PIP,Lead_SNP)
AFR_GWAS_plot_data<-candidate_region%>%mutate(r2 = r2_AFR,PIP = -log10(2*pnorm(-abs(zscore_BB))),Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS))%>%select(SNP,POS, r2,PIP,Lead_SNP)
p_EUR<-gwas_plot_fun (EUR_GWAS_plot_data, "UKBB", "-log10(P-value)", -log10(5e-8))
p_AFR<-gwas_plot_fun (AFR_GWAS_plot_data, "GLGC", "-log10(P-value)", -log10(5e-8))
###Finemap Plot
EUR_SuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = SuSiE_WB,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(SuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
AFR_SuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_AFR,PIP = SuSiE_BB,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(SuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
MESuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = MESuSiE_PIP_Either,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(MESuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
Paintor_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = Paintor_PIP,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(Paintor_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
p_EUR_SuSiE<-finemap_plot_fun(EUR_SuSiE_plot_data, "SuSiE UKBB", "PIP", 0.5)
p_AFR_SuSiE<-finemap_plot_fun(AFR_SuSiE_plot_data, "SuSiE GLGC", "PIP", 0.5)
p_MESuSiE<-finemap_plot_fun(MESuSiE_plot_data, "MESuSiE", "PIP", 0.5)
p_Paintor<-finemap_plot_fun(Paintor_plot_data, "Paintor", "PIP", 0.5)
# Gene Plot
plot.range <- c(min(candidate_region$POS), max(candidate_region$POS))
Gene_List_sub_coding<-Gene_List%>%filter(Chrom==paste0("chr",unique(candidate_region$CHR)))%>%filter(Start<max(candidate_region$POS),End>min(candidate_region$POS))%>%filter(Coding=="proteincoding")%>%filter(!is.na(cdsLength))%>%filter(GeneLength>15000)
p2<-gene_range_plot_fun(Gene_List_sub_coding,plot.range)
##Combine Plot together
combined_plot<-(p_EUR/p_EUR_SuSiE/p_MESuSiE/p2+plot_layout(heights = c(1,1,1,1.5))|p_AFR/p_AFR_SuSiE/p_Paintor/p2+plot_layout(heights = c(1,1,1,1.5)))+plot_layout(guides = 'collect')&theme(legend.position = "bottom")
combined_plot
3b. APOH Example
##############################################################
#
#
# APOH missense shared for TG (rs1801689)
#
################################################################
region = 438
trait_name = "TG"
ref_panel = "UKB1"
p_threshold_index = 1
res_z_dir<-paste0("/net/fantasia/home/borang/Susie_Mult/Revision_Round_1/01_02_Real_Data/",trait_name,"/",trait_name,"_REF_",ref_panel,"_P",p_threshold_index,"/")
out_dir<-paste0(res_z_dir,"data/")
out_res_dir<-paste0(res_z_dir,"res/")
##Date Reprocess
WB_COV<-as.matrix(fread(paste0(out_dir,"Region_",region,".LD1")))
BB_COV<-as.matrix(fread(paste0(out_dir,"Region_",region,".LD2")))
load(paste0(out_res_dir,"MESuSiE_region_",region,".RData"))
candidate_region<-res_all%>%filter(Region==region)
# rs1801689 is the missense of APOH gene, highlighted in the paper
lead_SNP = "rs1801689"
lead_SNP_index<-which(candidate_region$SNP==lead_SNP)
candidate_region<-candidate_region%>%mutate(r2_EUR = unname(unlist((WB_COV[,lead_SNP_index])^2)) ,r2_AFR = unname(unlist((BB_COV[,lead_SNP_index])^2)),POS = as.numeric(POS))
####Category Setting
candidate_region<-candidate_region%>%mutate(SuSiE_cat = case_when(SuSiE_WB>0.5&SuSiE_BB>0.5 ~ 3,
SuSiE_WB>0.5&SuSiE_BB<0.5 ~ 1,
SuSiE_WB<0.5&SuSiE_BB>0.5 ~ 2,
TRUE ~ 0),
Paintor_cat = case_when(Paintor_PIP>0.5~4,
TRUE~0),
MESuSiE_cat = case_when(MESuSiE_PIP_WB>0.5~1,
MESuSiE_PIP_BB>0.5~2,
MESuSiE_PIP_Shared>0.5~3,
TRUE~0))
###GWAS PLOT
EUR_GWAS_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = -log10(2*pnorm(-abs(zscore_WB))),Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS))%>%select(SNP,POS, r2,PIP,Lead_SNP)
AFR_GWAS_plot_data<-candidate_region%>%mutate(r2 = r2_AFR,PIP = -log10(2*pnorm(-abs(zscore_BB))),Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS))%>%select(SNP,POS, r2,PIP,Lead_SNP)
p_EUR<-gwas_plot_fun (EUR_GWAS_plot_data, "UKBB", "-log10(P-value)", -log10(5e-8))
p_AFR<-gwas_plot_fun (AFR_GWAS_plot_data, "GLGC", "-log10(P-value)", -log10(5e-8))
###Finemap Plot
EUR_SuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = SuSiE_WB,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(SuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
AFR_SuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_AFR,PIP = SuSiE_BB,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(SuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
MESuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = MESuSiE_PIP_Either,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(MESuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
Paintor_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = Paintor_PIP,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(Paintor_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
p_EUR_SuSiE<-finemap_plot_fun(EUR_SuSiE_plot_data, "SuSiE UKBB", "PIP", 0.5)
p_AFR_SuSiE<-finemap_plot_fun(AFR_SuSiE_plot_data, "SuSiE GLGC", "PIP", 0.5)
p_MESuSiE<-finemap_plot_fun(MESuSiE_plot_data, "MESuSiE", "PIP", 0.5)
p_Paintor<-finemap_plot_fun(Paintor_plot_data, "Paintor", "PIP", 0.5)
# Gene Plot
plot.range <- c(min(candidate_region$POS), max(candidate_region$POS))
Gene_List_sub_coding<-Gene_List%>%filter(Chrom==paste0("chr",unique(candidate_region$CHR)))%>%filter(Start<max(candidate_region$POS),End>min(candidate_region$POS))%>%filter(Coding=="proteincoding")%>%filter(!is.na(cdsLength))%>%filter(GeneLength>15000)
p2<-gene_range_plot_fun(Gene_List_sub_coding,plot.range)
##Combine Plot together
combined_plot<-(p_EUR/p_EUR_SuSiE/p_MESuSiE/p2+plot_layout(heights = c(1,1,1,1.5))|p_AFR/p_AFR_SuSiE/p_Paintor/p2+plot_layout(heights = c(1,1,1,1.5)))+plot_layout(guides = 'collect')&theme(legend.position = "bottom")
combined_plot
3c. TM6SF2 Example
#######################################################################
#
# TM6SF2 gene and LDL association with ancestry-specific effect
#
#
##########################################################################
region = 275
trait_name = "LDL"
ref_panel = "UKB1"
p_threshold_index = 1
res_z_dir<-paste0("/net/fantasia/home/borang/Susie_Mult/Revision_Round_1/01_02_Real_Data/",trait_name,"/",trait_name,"_REF_",ref_panel,"_P",p_threshold_index,"/")
out_dir<-paste0(res_z_dir,"data/")
out_res_dir<-paste0(res_z_dir,"res/")
##Date Reprocess
WB_COV<-as.matrix(fread(paste0(out_dir,"Region_",region,".LD1")))
BB_COV<-as.matrix(fread(paste0(out_dir,"Region_",region,".LD2")))
load(paste0(out_res_dir,"MESuSiE_region_",region,".RData"))
candidate_region<-res_all%>%filter(Region==region)
# rs58542926 is the missense of TM6SF2 gene gene, highlighted in the paper
lead_SNP = "rs58542926"
lead_SNP_index<-which(candidate_region$SNP==lead_SNP)
candidate_region<-candidate_region%>%mutate(r2_EUR = unname(unlist((WB_COV[,lead_SNP_index])^2)) ,r2_AFR = unname(unlist((BB_COV[,lead_SNP_index])^2)),POS = as.numeric(POS))
####Category Setting
candidate_region<-candidate_region%>%mutate(SuSiE_cat = case_when(SuSiE_WB>0.5&SuSiE_BB>0.5 ~ 3,
SuSiE_WB>0.5&SuSiE_BB<0.5 ~ 1,
SuSiE_WB<0.5&SuSiE_BB>0.5 ~ 2,
TRUE ~ 0),
Paintor_cat = case_when(Paintor_PIP>0.5~4,
TRUE~0),
MESuSiE_cat = case_when(MESuSiE_PIP_WB>0.5~1,
MESuSiE_PIP_BB>0.5~2,
MESuSiE_PIP_Shared>0.5~3,
TRUE~0))
###GWAS PLOT
EUR_GWAS_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = -log10(2*pnorm(-abs(zscore_WB))),Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS))%>%select(SNP,POS, r2,PIP,Lead_SNP)
AFR_GWAS_plot_data<-candidate_region%>%mutate(r2 = r2_AFR,PIP = -log10(2*pnorm(-abs(zscore_BB))),Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS))%>%select(SNP,POS, r2,PIP,Lead_SNP)
p_EUR<-gwas_plot_fun (EUR_GWAS_plot_data, "UKBB", "-log10(P-value)", -log10(5e-8))
p_AFR<-gwas_plot_fun (AFR_GWAS_plot_data, "GLGC", "-log10(P-value)", -log10(5e-8))
###Finemap Plot
EUR_SuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = SuSiE_WB,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(SuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
AFR_SuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_AFR,PIP = SuSiE_BB,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(SuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
MESuSiE_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = MESuSiE_PIP_Either,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(MESuSiE_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
Paintor_plot_data<-candidate_region%>%mutate(r2 = r2_EUR,PIP = Paintor_PIP,Lead_SNP = ifelse(SNP==lead_SNP,1,0),POS= as.numeric(POS),cat = factor(Paintor_cat,levels = c("0", "1", "2", "3", "4"), labels = c("Non", "EUR", "AFR", "Shared", "Paintor")))%>%select(SNP,POS, r2,PIP,Lead_SNP,cat)
p_EUR_SuSiE<-finemap_plot_fun(EUR_SuSiE_plot_data, "SuSiE UKBB", "PIP", 0.5)
p_AFR_SuSiE<-finemap_plot_fun(AFR_SuSiE_plot_data, "SuSiE GLGC", "PIP", 0.5)
p_MESuSiE<-finemap_plot_fun(MESuSiE_plot_data, "MESuSiE", "PIP", 0.5)
p_Paintor<-finemap_plot_fun(Paintor_plot_data, "Paintor", "PIP", 0.5)
# Gene Plot
plot.range <- c(min(candidate_region$POS), max(candidate_region$POS))
Gene_List_sub_coding<-Gene_List%>%filter(Chrom==paste0("chr",unique(candidate_region$CHR)))%>%filter(Start<max(candidate_region$POS),End>min(candidate_region$POS))%>%filter(Coding=="proteincoding")%>%filter(!is.na(cdsLength))%>%filter(GeneLength>15000|Gene=="TM6SF2")
p2<-gene_range_plot_fun(Gene_List_sub_coding,plot.range)
##Combine Plot together
combined_plot<-(p_EUR/p_EUR_SuSiE/p_MESuSiE/p2+plot_layout(heights = c(1,1,1,1.5))|p_AFR/p_AFR_SuSiE/p_Paintor/p2+plot_layout(heights = c(1,1,1,1.5)))+plot_layout(guides = 'collect')&theme(legend.position = "bottom")
combined_plot
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 mgcv_1.8-40
[13] getPass_0.2-2 callr_3.7.3 vctrs_0.6.2
[16] quantreg_5.95 crayon_1.5.2 pkgconfig_2.0.3
[19] fastmap_1.1.1 backports_1.4.1 labeling_0.4.2
[22] utf8_1.2.3 promises_1.2.0.1 rmarkdown_2.22
[25] ps_1.7.2 MatrixModels_0.5-1 purrr_1.0.1
[28] xfun_0.39 modeltools_0.2-23 cachem_1.0.8
[31] aplot_0.1.10 jsonlite_1.8.3 highr_0.10
[34] later_1.3.1 reshape_0.8.9 irlba_2.3.5.1
[37] broom_1.0.5 parallel_4.3.1 R6_2.5.1
[40] bslib_0.5.0 stringi_1.7.12 car_3.1-2
[43] boot_1.3-28.1 jquerylib_0.1.4 cellranger_1.1.0
[46] Rcpp_1.0.11 knitr_1.39 zoo_1.8-12
[49] httpuv_1.6.11 Matrix_1.5-4.1 splines_4.3.1
[52] tidyselect_1.2.0 rstudioapi_0.14 abind_1.4-5
[55] yaml_2.3.7 codetools_0.2-19 processx_3.8.0
[58] plyr_1.8.8 lattice_0.20-45 tibble_3.2.1
[61] withr_2.5.1 evaluate_0.18 gridGraphics_0.5-1
[64] lambda.r_1.2.4 proxy_0.4-27 pillar_1.9.0
[67] carData_3.0-5 whisker_0.4.1 stats4_4.3.1
[70] ggfun_0.0.9 generics_0.1.3 rprojroot_2.0.3
[73] munsell_0.5.0 scales_1.2.1 rootSolve_1.8.2.3
[76] class_7.3-20 glue_1.6.2 lmom_2.8
[79] tools_4.3.1 SparseM_1.81 ggsignif_0.6.4
[82] Exact_3.1 fs_1.6.2 mvtnorm_1.1-3
[85] libcoin_1.0-9 colorspace_2.1-0 nlme_3.1-157
[88] cli_3.6.1 futile.options_1.0.1 fansi_1.0.5
[91] expm_0.999-7 mixsqp_0.3-48 gtable_0.3.1
[94] rstatix_0.7.2 yulab.utils_0.0.4 sass_0.4.6
[97] digest_0.6.30 TH.data_1.1-2 ggplotify_0.1.0
[100] farver_2.1.1 htmltools_0.5.5 lifecycle_1.0.3
[103] httr_1.4.6 MASS_7.3-57