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s09.Microbial_GWAS_fmetab.Rmd
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s09.Microbial_GWAS_fmetab.Rmd
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---
title: "Microbial GWAS"
author: "Daoming Wang"
date: "2021/10/21"
output:
html_document:
theme: flatly
highlight: espresso
toc: true
toc_depth: 4
toc_float: true
word_document: default
pdf_document:
includes:
in_header: header.tex
keep_tex: yes
latex_engine: xelatex
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## 1 Preparation
### 1.1 Import
Import packages and functions.
```{r 1.1, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
suppressMessages(source("functions.R"))
```
### 1.2 Inputs
Read input files.
```{r 1.2, echo=TRUE}
load("01.cleanData/LLD/lld_dsv.RData")
load("01.cleanData/LLD/lld_vsv.RData")
load("01.cleanData/LLD/lld_fmetab.RData")
load("01.cleanData/LLD/lld_phen.RData")
load("01.cleanData/LLD/lld_abun.RData")
load("01.cleanData/LLD/lld_msv_pc_cum0.6.RData")
load("01.cleanData/IBD/ibd_dsv.RData")
load("01.cleanData/IBD/ibd_vsv.RData")
load("01.cleanData/IBD/ibd_fmetab.RData")
load("01.cleanData/IBD/ibd_phen.RData")
load("01.cleanData/IBD/ibd_abun.RData")
load("01.cleanData/IBD/ibd_msv_pc_cum0.6.RData")
info <- read.table("01.cleanData/Info/Informative_species_information.tsv",
sep = "\t", header = T, stringsAsFactors = F)
vsv_info<-read.table("01.cleanData/Info/vsgv_info_anno.tsv",
sep = "\t",header = T,stringsAsFactors = F, quote = "")
dsv_info<-read.table("01.cleanData/Info/dsgv_info_anno.tsv",
sep = "\t",header = T,stringsAsFactors = F,quote = "")
fmetab_info<-read.csv("01.cleanData/Info/fmetab_info_addIBD.tsv", sep = "\t",header = T, check.names = F,quote = "")
```
## 2 Associations between SVs and fecal metabolites
### 2.1 Preparation
```{r 2.1}
if (!dir.exists("09.Microbial_GWAS_fmetab")) {dir.create("09.Microbial_GWAS_fmetab")}
if (!dir.exists("09.Microbial_GWAS_fmetab/RData")) {dir.create("09.Microbial_GWAS_fmetab/RData")}
# lld
lld_abun_clr<-abundances(x=as.data.frame(na.omit(lld_abun)), transform="clr") %>% as.data.frame
lld_abun_clr <- lld_abun_clr[match(rownames(lld_abun), rownames(lld_abun_clr)),]
rownames(lld_abun_clr) <- rownames(lld_abun)
lld_covar<-cbind(lld_phen,lld_abun_clr)
# ibd
ibd_abun_clr<-abundances(x=as.data.frame(na.omit(ibd_abun)), transform="clr") %>% as.data.frame
ibd_abun_clr <- ibd_abun_clr[match(rownames(ibd_abun), rownames(ibd_abun_clr)),]
rownames(ibd_abun_clr) <- rownames(ibd_abun)
ibd_covar<-cbind(ibd_phen,ibd_abun_clr)
covar <- c("LC.COLUMN", "Amount_sample_gram", "metabolon_Month_in_freezer","host_Sex", "host_Age", "host_BMI", "Read_count")
```
### 2.2 linear model 1
Linear model with covariates: age, gender, BMI, read count and corresponding species relative abundance.
```{r 2.2, eval = FALSE}
## vsv
lld_vsv_fmetab_lm_adjAbun_res<-lm_btw_mats_adjAbun(lld_fmetab,lld_vsv,lld_phen,covar,lld_abun_clr,info, 9)
save(lld_vsv_fmetab_lm_adjAbun_res, file = "09.Microbial_GWAS_fmetab/RData/lld_vsv_fmetab_lm_adjAbun_res.RData")
ibd_vsv_fmetab_lm_adjAbun_res<-lm_btw_mats_adjAbun(ibd_fmetab,ibd_vsv,ibd_phen,covar,ibd_abun_clr,info,9)
save(ibd_vsv_fmetab_lm_adjAbun_res, file = "09.Microbial_GWAS_fmetab/RData/ibd_vsv_fmetab_lm_adjAbun_res.RData")
# meta-analysis
cbind_vsv_fmetab_lm_adjAbun_edge<-cbind(lld_vsv_fmetab_lm_adjAbun_res, ibd_vsv_fmetab_lm_adjAbun_res)[,-c(16,17)]
colnames(cbind_vsv_fmetab_lm_adjAbun_edge)<-c("BA","SV",
paste("LLD.",colnames(cbind_vsv_fmetab_lm_adjAbun_edge)[3:15],sep = ""),
paste("IBD.",colnames(cbind_vsv_fmetab_lm_adjAbun_edge)[3:15],sep = ""))
all_vsv_fmetab_lm_adjAbun_res <- my_batch_meta_lm(cbind_vsv_fmetab_lm_adjAbun_edge, c("LLD", "300OB"), c(3,16), c(4,17))
save(all_vsv_fmetab_lm_adjAbun_res, file = "09.Microbial_GWAS_fmetab/RData/all_vsv_fmetab_lm_adjAbun_res.RData")
## dsv
lld_dsv_fmetab_lm_adjAbun_res<-lm_btw_mats_adjAbun(lld_fmetab,lld_dsv,lld_phen,covar,lld_abun_clr,info, 9)
save(lld_dsv_fmetab_lm_adjAbun_res, file = "09.Microbial_GWAS_fmetab/RData/lld_dsv_fmetab_lm_adjAbun_res.RData")
ibd_dsv_fmetab_lm_adjAbun_res<-lm_btw_mats_adjAbun(ibd_fmetab,ibd_dsv,ibd_phen,covar,ibd_abun_clr,info,9)
save(ibd_dsv_fmetab_lm_adjAbun_res, file = "09.Microbial_GWAS_fmetab/RData/ibd_dsv_fmetab_lm_adjAbun_res.RData")
# meta-analysis
cbind_dsv_fmetab_lm_adjAbun_edge<-cbind(lld_dsv_fmetab_lm_adjAbun_res, ibd_dsv_fmetab_lm_adjAbun_res)[,-c(16,17)]
colnames(cbind_dsv_fmetab_lm_adjAbun_edge)<-c("BA","SV",
paste("LLD.",colnames(cbind_dsv_fmetab_lm_adjAbun_edge)[3:15],sep = ""),
paste("IBD.",colnames(cbind_dsv_fmetab_lm_adjAbun_edge)[3:15],sep = ""))
all_dsv_fmetab_lm_adjAbun_res <- my_batch_meta_lm(cbind_dsv_fmetab_lm_adjAbun_edge, c("LLD", "300OB"), c(3,16), c(4,17))
save(all_dsv_fmetab_lm_adjAbun_res, file = "09.Microbial_GWAS_fmetab/RData/all_dsv_fmetab_lm_adjAbun_res.RData")
```
### 2.3 linear model 2
Linear model with covariates: age, gender, BMI, read count, corresponding species relative abundance, and genetic structure PCs.
```{r 2.3, eval=FALSE}
## vsv
lld_vsv_fmetab_lm_adjAbunPCs_res <- lm_btw_mats_adjAbunPCs(lld_fmetab,lld_vsv,lld_phen,covar,lld_abun_clr,lld_msv_pc_cum0.6,info, 9)
save(lld_vsv_fmetab_lm_adjAbunPCs_res, file = "09.Microbial_GWAS/RData/lld_vsv_fmetab_lm_adjAbunPCs_res.RData")
ibd_vsv_fmetab_lm_adjAbunPCs_res <- lm_btw_mats_adjAbunPCs(ibd_fmetab, ibd_vsv, ibd_phen, covar, ibd_abun_clr, ibd_msv_pc_cum0.6, info, 9)
save(ibd_vsv_fmetab_lm_adjAbunPCs_res, file = "09.Microbial_GWAS/RData/ibd_vsv_fmetab_lm_adjAbunPCs_res.RData")
# meta-analysis
cbind_vsv_fmetab_lm_adjAbunPCs_edge<-cbind(lld_vsv_fmetab_lm_adjAbunPCs_res, ibd_vsv_fmetab_lm_adjAbunPCs_res)[,-c(16,17)]
colnames(cbind_vsv_fmetab_lm_adjAbunPCs_edge)<-c("BA","SV",
paste("LLD.",colnames(cbind_vsv_fmetab_lm_adjAbunPCs_edge)[3:15],sep = ""),
paste("X300OB.",colnames(cbind_vsv_fmetab_lm_adjAbunPCs_edge)[3:15],sep = ""))
all_vsv_fmetab_lm_adjAbunPCs_res <- my_batch_meta_lm(cbind_vsv_fmetab_lm_adjAbunPCs_edge, c("LLD", "IBD"), c(3,16), c(4,17))
save(all_vsv_fmetab_lm_adjAbunPCs_res, file = "09.Microbial_GWAS/RData/all_vsv_fmetab_lm_adjAbunPCs_res.RData")
## dsv
lld_dsv_fmetab_lm_adjAbunPCs_res <- lm_btw_mats_adjAbunPCs(lld_fmetab,lld_dsv,lld_phen,covar,lld_abun_clr,lld_msv_pc_cum0.6,info, 9)
save(lld_dsv_fmetab_lm_adjAbunPCs_res, file = "09.Microbial_GWAS/RData/lld_dsv_fmetab_lm_adjAbunPCs_res.RData")
ibd_dsv_fmetab_lm_adjAbunPCs_res <- lm_btw_mats_adjAbunPCs(ibd_fmetab, ibd_dsv, ibd_phen, covar, ibd_abun_clr, ibd_msv_pc_cum0.6, info, 9)
save(ibd_dsv_fmetab_lm_adjAbunPCs_res, file = "09.Microbial_GWAS/RData/ibd_dsv_fmetab_lm_adjAbunPCs_res.RData")
# meta-analysis
cbind_dsv_fmetab_lm_adjAbunPCs_edge<-cbind(lld_dsv_fmetab_lm_adjAbunPCs_res, ibd_dsv_fmetab_lm_adjAbunPCs_res)[,-c(16,17)]
colnames(cbind_dsv_fmetab_lm_adjAbunPCs_edge)<-c("BA","SV",
paste("LLD.",colnames(cbind_dsv_fmetab_lm_adjAbunPCs_edge)[3:15],sep = ""),
paste("X300OB.",colnames(cbind_dsv_fmetab_lm_adjAbunPCs_edge)[3:15],sep = ""))
all_dsv_fmetab_lm_adjAbunPCs_res <- my_batch_meta_lm(cbind_dsv_fmetab_lm_adjAbunPCs_edge, c("LLD", "IBD"), c(3,16), c(4,17))
save(all_dsv_fmetab_lm_adjAbunPCs_res, file = "09.Microbial_GWAS/RData/all_dsv_fmetab_lm_adjAbunPCs_res.RData")
```
## 3 Summary results
### 3.1 vSV
```{r 3.1}
load("09.Microbial_GWAS_fmetab/RData/all_vsv_fmetab_lm_adjAbun_res.RData")
load("09.Microbial_GWAS_fmetab/RData/all_vsv_fmetab_lm_adjAbunPCs_res.RData")
colnames(all_vsv_fmetab_lm_adjAbun_res)[1] <- "Fecal_metabolite"
colnames(all_vsv_fmetab_lm_adjAbunPCs_res)[1] <- "Fecal_metabolite"
colnames(all_vsv_fmetab_lm_adjAbun_res)[c(3:ncol(all_vsv_fmetab_lm_adjAbun_res))]<-paste(
"Model1.",
colnames(all_vsv_fmetab_lm_adjAbun_res)[c(3:ncol(all_vsv_fmetab_lm_adjAbun_res))],
sep = "")
colnames(all_vsv_fmetab_lm_adjAbunPCs_res)[c(3:ncol(all_vsv_fmetab_lm_adjAbunPCs_res))]<-paste(
"Model2.",
colnames(all_vsv_fmetab_lm_adjAbunPCs_res)[c(3:ncol(all_vsv_fmetab_lm_adjAbunPCs_res))],
sep = "")
vsv_fmetab<-cbind(all_vsv_fmetab_lm_adjAbun_res, all_vsv_fmetab_lm_adjAbunPCs_res[,-c(1:2)])
vsv_fmetab$Fecal_metabolite<-as.character(vsv_fmetab$Fecal_metabolite)
## replicable associations
vsv_fmetab.sig<-vsv_fmetab[vsv_fmetab$Model1.Meta.fdr.p<0.05 & (!is.na(vsv_fmetab$Model1.Meta.fdr.p)) &
vsv_fmetab$Model1.Meta.hetero.p>0.05 & (!is.na(vsv_fmetab$Model1.Meta.hetero.p)) &
vsv_fmetab$Model1.LLD.p<0.05 & (!is.na(vsv_fmetab$Model1.LLD.p)) &
vsv_fmetab$Model1.IBD.p<0.05 & (!is.na(vsv_fmetab$Model1.IBD.p)) &
vsv_fmetab$Model2.Meta.p<0.05 & (!is.na(vsv_fmetab$Model2.Meta.p)) &
(!grepl("X__",vsv_fmetab$Fecal_metabolite)),]
vsv_fmetab.sig.anno<-left_join(vsv_fmetab.sig, vsv_info, by = c("SV" = "SV_Name")) %>% left_join(., fmetab_info, by = c("Fecal_metabolite"="all_fmetab.name"))
#str_replace_all(vsv_fmetab.sig$SV, ":.*", "") %>% unique %>% length
save(vsv_fmetab.sig.anno, file = "09.Microbial_GWAS_fmetab/vsv_fmetab.sig.anno.RData")
write.table(vsv_fmetab.sig.anno,"09.Microbial_GWAS_fmetab/vsv_fmetab.sig.anno.tsv",sep = "\t",col.names = T, row.names = F, quote = F)
## heterogeneous associations
vsv_fmetab.het<-vsv_fmetab[vsv_fmetab$Model1.Meta.I2>0.75 & (!is.na(vsv_fmetab$Model1.Meta.I2)) &
vsv_fmetab$Model1.Meta.hetero.p<0.05 & (!is.na(vsv_fmetab$Model1.Meta.hetero.p)) &
((vsv_fmetab$Model1.LLD.fdr.p<0.05 & (!is.na(vsv_fmetab$Model1.LLD.fdr.p))) |
(vsv_fmetab$Model1.IBD.fdr.p<0.05 & (!is.na(vsv_fmetab$Model1.IBD.fdr.p)))) &
(!grepl("X__",vsv_fmetab$Fecal_metabolite)),]
vsv_fmetab.het.anno<-left_join(vsv_fmetab.het, vsv_info, by = c("SV" = "SV_Name"))%>% left_join(., fmetab_info, by = c("Fecal_metabolite"="all_fmetab.name"))
save(vsv_fmetab.het.anno, file = "09.Microbial_GWAS_fmetab/vsv_fmetab.het.anno.RData")
write.table(vsv_fmetab.het.anno,"09.Microbial_GWAS_fmetab/vsv_fmetab.het.anno.tsv",sep = "\t",col.names = T, row.names = F, quote = F)
```
### 3.2 dSV
```{r 3.2}
load("09.Microbial_GWAS_fmetab/RData/all_dsv_fmetab_lm_adjAbun_res.RData")
load("09.Microbial_GWAS_fmetab/RData/all_dsv_fmetab_lm_adjAbunPCs_res.RData")
colnames(all_dsv_fmetab_lm_adjAbun_res)[1] <- "Fecal_metabolite"
colnames(all_dsv_fmetab_lm_adjAbunPCs_res)[1] <- "Fecal_metabolite"
colnames(all_dsv_fmetab_lm_adjAbun_res)[c(3:ncol(all_dsv_fmetab_lm_adjAbun_res))]<-paste(
"Model1.",
colnames(all_dsv_fmetab_lm_adjAbun_res)[c(3:ncol(all_dsv_fmetab_lm_adjAbun_res))],
sep = "")
colnames(all_dsv_fmetab_lm_adjAbunPCs_res)[c(3:ncol(all_dsv_fmetab_lm_adjAbunPCs_res))]<-paste(
"Model2.",
colnames(all_dsv_fmetab_lm_adjAbunPCs_res)[c(3:ncol(all_dsv_fmetab_lm_adjAbunPCs_res))],
sep = "")
dsv_fmetab<-cbind(all_dsv_fmetab_lm_adjAbun_res, all_dsv_fmetab_lm_adjAbunPCs_res[,-c(1:2)])
dsv_fmetab$Fecal_metabolite<-as.character(dsv_fmetab$Fecal_metabolite)
## replicable associations
dsv_fmetab.sig<-dsv_fmetab[dsv_fmetab$Model1.Meta.fdr.p<0.05 & (!is.na(dsv_fmetab$Model1.Meta.fdr.p)) &
dsv_fmetab$Model1.Meta.hetero.p>0.05 & (!is.na(dsv_fmetab$Model1.Meta.hetero.p)) &
dsv_fmetab$Model1.LLD.p<0.05 & (!is.na(dsv_fmetab$Model1.LLD.p)) &
dsv_fmetab$Model1.IBD.p<0.05 & (!is.na(dsv_fmetab$Model1.IBD.p)) &
dsv_fmetab$Model2.Meta.p<0.05 & (!is.na(dsv_fmetab$Model2.Meta.p)) &
(!grepl("X__",dsv_fmetab$Fecal_metabolite)),]
dsv_fmetab.sig.anno<-left_join(dsv_fmetab.sig, dsv_info, by = c("SV" = "SV_Name")) %>% left_join(., fmetab_info, by = c("Fecal_metabolite"="all_fmetab.name"))
#str_replace_all(dsv_fmetab.sig$SV, ":.*", "") %>% unique %>% length
save(dsv_fmetab.sig.anno, file = "09.Microbial_GWAS_fmetab/dsv_fmetab.sig.anno.RData")
write.table(dsv_fmetab.sig.anno,"09.Microbial_GWAS_fmetab/dsv_fmetab.sig.anno.tsv",sep = "\t",col.names = T, row.names = F, quote = F)
## heterogeneous associations
dsv_fmetab.het<-dsv_fmetab[dsv_fmetab$Model1.Meta.I2>0.75 & (!is.na(dsv_fmetab$Model1.Meta.I2)) &
dsv_fmetab$Model1.Meta.hetero.p<0.05 & (!is.na(dsv_fmetab$Model1.Meta.hetero.p)) &
((dsv_fmetab$Model1.LLD.fdr.p<0.05 & (!is.na(dsv_fmetab$Model1.LLD.fdr.p))) |
(dsv_fmetab$Model1.IBD.fdr.p<0.05 & (!is.na(dsv_fmetab$Model1.IBD.fdr.p)))) &
(!grepl("X__",dsv_fmetab$Fecal_metabolite)),]
dsv_fmetab.het.anno<-left_join(dsv_fmetab.het, dsv_info, by = c("SV" = "SV_Name")) %>% left_join(., fmetab_info, by = c("Fecal_metabolite"="all_fmetab.name"))
save(dsv_fmetab.het.anno, file = "09.Microbial_GWAS_fmetab/dsv_fmetab.het.anno.RData")
write.table(dsv_fmetab.het.anno,"09.Microbial_GWAS_fmetab/dsv_fmetab.het.anno.tsv",sep = "\t",col.names = T, row.names = F, quote = F)
```
### 2.2 Merge results
```{r 2.2}
load("09.Microbial_GWAS_fmetab/vsv_fmetab.sig.anno.RData")
load("09.Microbial_GWAS_fmetab/dsv_fmetab.sig.anno.RData")
sv_fmetab.sig.anno <- rbind(vsv_fmetab.sig.anno,dsv_fmetab.sig.anno)
sv_fmetab.sig.subpath <- sv_fmetab.sig.anno$Annotation%>%
as.character(.) %>%
.[!duplicated(.)]
sv_fmetab.sig.sv <- sv_fmetab.sig.anno$SV %>%
as.character(.) %>%
.[!duplicated(.)] %>%
sort(.,decreasing = F)
## circos plot
sv_fmetab_count<-NULL
for (pheno in sv_fmetab.sig.subpath) {
# pheno <-"Polyamine Metabolism"
sv_phen_df<-sv_fmetab.sig.anno[sv_fmetab.sig.anno$Annotation==pheno,]$SV %>%
str_replace_all(.,"\\:\\d+_\\d+.*", "") %>%
table %>%
as.data.frame
colnames(sv_phen_df)<-c("Species", "Count")
sv_phen_df<-data.frame(Fecal_metabolite = rep(pheno,nrow(sv_phen_df)), sv_phen_df)
sv_fmetab_count<-rbind(sv_fmetab_count,sv_phen_df)
}
sv_fmetab_count$Species<-info$Short_name[match(sv_fmetab_count$Species, info$organism)]
sv_fmetab_count <- sv_fmetab_count[order(sv_fmetab_count$Count),]
sv_fmetab_count_species_order<-sv_fmetab_count %>% group_by(Species) %>% summarise(sum(Count)) %>% .[order(.$`sum(Count)`, decreasing = T),] %>% .$Species %>% as.character
sv_fmetab_count_fmetab_order<-sv_fmetab_count %>% group_by(Fecal_metabolite) %>% summarise(sum(Count)) %>% .[order(.$`sum(Count)`, decreasing = T),] %>% .$Fecal_metabolite %>% as.character
pdf("09.Microbial_GWAS_fmetab/sv_fmetab.subpath.circos.pdf", width = 62, height = 62)
circos.par(start.degree = 0) # ,xaxis.clock.wise = F , "clock.wise" = F
chordDiagram(sv_fmetab_count,annotationTrack = "grid",
grid.col = c(rev(wes_palette("Darjeeling1", length(sv_fmetab_count_fmetab_order), type = "continuous")),
rep('grey',length(sv_fmetab_count_species_order))),
order = c(sv_fmetab_count_fmetab_order,
rev(sv_fmetab_count_species_order)),
big.gap = 5,
preAllocateTracks = list(track.margin = c(0, uh(260, "mm")),
track.height = max(strwidth(unlist(dimnames(sv_fmetab_count)))))
)
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,cex = 2.5,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))
}, bg.border = NA) # here set bg.border to NA is important
dev.off()
circos.clear()
pathway_df<-sv_fmetab.sig.anno$Fecal_metabolite %>% unique %>% match(.,fmetab_info$fmetab_anno.name) %>%na.omit()%>%fmetab_info[.,c(5,24)]%>%table%>%as.data.frame
p_sv_metabolites<-ggplot(pathway_df, aes(x=Super.pathway, y=Freq, group = IBD_associated, fill = IBD_associated))+
geom_bar(position = "stack",stat="identity")+
xlab("KEGG pathways")+
ylab("Number of SV associated metabolites")+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(name= "IBD-associated",
breaks = c("Yes", "No"),
labels = c("Yes", "No"),
values = rev(mycolor2_green_blue))+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
plot.margin=unit(c(1,1,1,1),"cm"),
axis.line.x = element_line(),
axis.line.y = element_line(),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position="top",
legend.key = element_rect(fill = NA),
legend.text = element_text(size = 10),
panel.grid.major = element_line(colour = NA,linetype = "dashed",size = 0.25),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("09.Microbial_GWAS_fmetab/SV_pathway_IBD.pdf", height = 5, width = 5)
print(p_sv_metabolites)
dev.off()
print(p_sv_metabolites)
```