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s06.species_level_assoc.Rmd
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s06.species_level_assoc.Rmd
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---
title: "Species level association"
author: "Daoming Wang"
date: "2021/10/9"
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
```{r 1.1, echo=TRUE, message=FALSE, warning=FALSE, paged.print=FALSE}
source("functions.R")
```
### 1.2 Inputs
```{r 1.2}
info <- read.table("01.cleanData/Info/Informative_species_information.tsv",
sep = "\t", header = T, stringsAsFactors = F)
fmetab_info<-read.csv("01.cleanData/Info/fmetab_info.tsv",sep = "\t",header = T,quote = "",check.names = F)
load("01.cleanData/All/all_phen.RData")
load("01.cleanData/All/all_fmetab.RData")
load("01.cleanData/All/all_abun.RData")
load("01.cleanData/All/distMat/all_msv_dist_std.RData")
load("01.cleanData/LLD/lld_phen.RData")
load("01.cleanData/LLD/lld_fmetab.RData")
load("01.cleanData/LLD/lld_abun.RData")
load("01.cleanData/LLD/distMat/lld_msv_dist_std.RData")
load("01.cleanData/IBD/ibd_phen.RData")
load("01.cleanData/IBD/ibd_fmetab.RData")
load("01.cleanData/IBD/ibd_abun.RData")
load("01.cleanData/IBD/distMat/ibd_msv_dist_std.RData")
```
## 2 Species-level association with fecal metabolites
### 2.1 Prepare abundance table
```{r 2.1}
if (!dir.exists("06.Species_level_assoc")) {dir.create("06.Species_level_assoc")}
if (!dir.exists("06.Species_level_assoc/RData")) {dir.create("06.Species_level_assoc/RData")}
## Prepare covariate table
# all
all_abun_clr<-abundances(x=as.data.frame(na.omit(all_abun)), transform="clr") %>% as.data.frame
all_abun_clr <- all_abun_clr[match(rownames(all_abun), rownames(all_abun_clr)),]
rownames(all_abun_clr) <- rownames(all_abun)
all_covar<-cbind(all_phen,all_abun_clr)
# 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)
covar1 <- c("LC.COLUMN", "Amount_sample_gram", "metabolon_Month_in_freezer","host_Sex", "host_Age", "host_BMI", "Read_count")
covar2 <- c("LC.COLUMN", "Amount_sample_gram", "metabolon_Month_in_freezer","host_Sex", "host_Age", "host_BMI", "Read_count", "IBD")
```
### 2.2 Association between genetic makeup and fecal metabolites
```{r 2.2,eval=F}
## all
all_adonis_res <- my_adonis_terms_adjAbun(all_msv_dist_std, all_fmetab, all_covar, covar2, info)
save(all_adonis_res, file = "06.Species_level_assoc/RData/all_adonis_res.RData")
lld_adonis_res <- my_adonis_terms_adjAbun(lld_msv_dist_std, lld_fmetab, lld_covar, covar1, info)
save(lld_adonis_res, file = "06.Species_level_assoc/RData/lld_adonis_res.RData")
ibd_adonis_res <- my_adonis_terms_adjAbun(ibd_msv_dist_std[c(20)], ibd_fmetab, ibd_covar, covar1, info)
save(ibd_adonis_res, file = "06.Species_level_assoc/RData/ibd_adonis_res.RData")
## meta-analysis
load("06.Species_level_assoc/RData/all_adonis_res.RData")
load("06.Species_level_assoc/RData/lld_adonis_res.RData")
load("06.Species_level_assoc/RData/ibd_adonis_res.RData")
all_adonis_res.table <- all_adonis_res$table
lld_adonis_res.table <- lld_adonis_res$table
ibd_adonis_res.table <- ibd_adonis_res$table
cbind_adonis_res.table<-cbind(all_adonis_res.table,lld_adonis_res.table,ibd_adonis_res.table)[,-c(8,9,15,16)]
colnames(cbind_adonis_res.table)<-c("Species","Fecal_metabolite",
paste("All.",colnames(cbind_adonis_res.table)[3:7],sep = ""),
paste("LLD.",colnames(cbind_adonis_res.table)[3:7],sep = ""),
paste("IBD.",colnames(cbind_adonis_res.table)[3:7],sep = ""))
adonis_res <- my_batch_meta_p(cbind_adonis_res.table, c("LLD", "IBD"), c(10,15), c(9,14))
save(adonis_res, file = "06.Species_level_assoc/RData/adonis_res.RData")
```
### 2.3 Association between abundance and fecal metabolites
```{r 2.3, eval=F}
lld_abun_fmetab_lm_res <- lm_btw_mats(lld_fmetab, lld_abun_clr[,info$organism], lld_covar, covar1)
ibd_abun_fmetab_lm_res <- lm_btw_mats(ibd_fmetab, ibd_abun_clr[,info$organism], ibd_covar, covar1)
# meta-analysis
cbind_abun_fmetab_lm_edge<-cbind(lld_abun_fmetab_lm_res, ibd_abun_fmetab_lm_res)[,-c(15,16)]
colnames(cbind_abun_fmetab_lm_edge)<-c("Fecal_metabolite","Species",
paste("LLD.",colnames(cbind_abun_fmetab_lm_edge)[3:14],sep = ""),
paste("IBD",colnames(cbind_abun_fmetab_lm_edge)[3:14],sep = ""))
all_abun_fmetab_lm_res <- my_batch_meta_lm(cbind_abun_fmetab_lm_edge, c("LLD", "IBD"), c(3,15), c(4,16))
save(all_abun_fmetab_lm_res, file = "06.Species_level_assoc/RData/all_abun_fmetab_lm_res.RData")
```
## 3 Species-level association with IBD
### 3.1 Association between SV and IBD
```{r 3.1,eval=F}
##all
all_disease_adonis_res <- my_adonis_terms_adjAbun(all_msv_dist_std, as.data.frame(all_phen[,c("IBD")]), all_covar, covar1, info,n_parallel = 4,n_permutations = 9999)
all_disease_adonis_res$table$BA<-"IBD"
colnames(all_disease_adonis_res$table)[2]<-"IBD"
colnames(all_disease_adonis_res$r2)<-"IBD"
colnames(all_disease_adonis_res$P)<-"IBD"
colnames(all_disease_adonis_res$FDR)<-"IBD"
save(all_disease_adonis_res, file = "06.Species_level_assoc/RData/all_disease_adonis_res.RData")
```
### 3.2 Association between abundance and IBD
```{r 3.2,eval=F}
all_disease_abun_lm_res<-lm_btw_mats_v2(all_abun_clr[,info$organism], as.data.frame(all_phen[,c("IBD")]), all_covar, covar1) # [,info$organism]
all_disease_abun_lm_res$X<-"IBD"
write.table(all_disease_abun_lm_res, "06.Species_level_assoc/all_disease_abun_lm_res.tsv",sep = "\t", col.names = T, row.names = F, quote = F)
all_disease_abun_lm.sig<-all_disease_abun_lm_res[all_disease_abun_lm_res$fdr.p<0.05 & (!is.na(all_disease_abun_lm_res$fdr.p)),]
write.table(all_disease_abun_lm.sig, "06.Species_level_assoc/all_disease_abun_lm.sig.tsv",sep = "\t", col.names = T, row.names = F, quote = F)
```
### 3.3 Association between fecal metabolites and IBD
```{r 3.3}
all_disease_fmetab_lm_res<-lm_btw_mats_v2(all_fmetab, as.data.frame(all_phen[,c("IBD")]), all_covar, covar1)
all_disease_fmetab_lm_res$X<-"IBD"
write.table(all_disease_fmetab_lm_res,"06.Species_level_assoc/all_disease_fmetab_lm_res.tsv",sep = "\t", col.names = T, row.names = F, quote = F)
fmetab_info$IBD_associated<-"No"
fmetab_info$IBD_associated[all_disease_fmetab_lm_res$fdr.p<0.05 & (!is.na(all_disease_fmetab_lm_res$fdr.p))]<-"Yes"
write.table(fmetab_info,"01.cleanData/Info/fmetab_info_addIBD.tsv",sep = "\t", col.names = T, row.names = F, quote = F)
all_disease_fmetab_lm.sig<-all_disease_fmetab_lm_res[all_disease_fmetab_lm_res$fdr.p<0.05 & (!is.na(all_disease_fmetab_lm_res$fdr.p)),]
write.table(all_disease_fmetab_lm.sig, "06.Species_level_assoc/all_disease_fmetab_lm.sig.tsv",sep = "\t", col.names = T, row.names = F, quote = F)
```
## 4 Summary and visualization
### 4.1 Preparation
```{r 4.1}
fmetab_info<-read.csv("01.cleanData/Info/fmetab_info_addIBD.tsv",sep = "\t",header = T,quote = "",check.names = F)
load("06.Species_level_assoc/RData/adonis_res.RData")
load( "06.Species_level_assoc/RData/all_disease_adonis_res.RData")
all_disease_adonis<-all_disease_adonis_res$table
write.table(all_disease_adonis, "06.Species_level_assoc/all_disease_adonis.tsv",
sep = "\t", col.names = T, row.names = F, quote = F)
species_r_n_order<- all_disease_adonis$Species[order(all_disease_adonis$R2, decreasing = T)]
#species_sgv_n_long<-gather(info[,c(10,12,13)], "type", "number", c(2:3))
p_r_disease_species<-ggplot(all_disease_adonis, aes(x=Species, y=R2))+
geom_bar(aes(fill = R2),position = "stack",stat="identity")+
xlab(NULL)+
ylab("R-square")+
scale_x_discrete(limits = species_r_n_order)+
scale_y_continuous(expand = c(0,0))+
scale_fill_gradientn(colours = wes_palette("Zissou1", 100, type = "continuous"))+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
plot.margin=unit(c(1,1,1,1),"cm"),
axis.line.x = element_line(),
axis.line.y = element_line(),
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position="none",
legend.key = element_rect(fill = NA),
legend.text = element_text(size = 10),
panel.grid.major = element_line(colour = NA,linetype = "dashed",size = 0.25),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("06.Species_level_assoc/p_r_disease_species.pdf", height = 3, width = 6)
print(p_r_disease_species)
dev.off()
print(p_r_disease_species)
```
### 4.2 Genetic makeup and IBD
### 4.3 Species abundance and IBD
### 4.4 Genetic make up and fecal metabolites
```{r 4.4}
adonis_res.table<-adonis_res$table
adonis_res.table.anno<-full_join(adonis_res.table, fmetab_info, by = c("Fecal_metabolite"="all_fmetab.name"))
adonis_res.table.anno.sig<-adonis_res.table.anno[adonis_res.table.anno$Meta.p<0.001 &
adonis_res.table.anno$LLD.P<0.05 &
adonis_res.table.anno$IBD.P<0.05 &
adonis_res.table.anno$Meta.hetero.p>0.05 &
(!is.na(adonis_res.table.anno$Meta.p<0.001 & adonis_res.table.anno$Meta.hetero.p>0.05)) &
(!grepl("X__",adonis_res.table.anno$Fecal_metabolite)),]
adonis_fmetab.sig.phen <- adonis_res.table.anno.sig$`SUB PATHWAY`%>%
as.character(.) %>%
.[!duplicated(.)]
adonis_fmetab.sig.taxa <- adonis_res.table.anno.sig$Species %>%
as.character(.) %>%
.[!duplicated(.)] %>%
sort(.,decreasing = F)
## circos plot
adonis_fmetab_count<-NULL
for (pheno in adonis_fmetab.sig.phen) {
# pheno <-"deoxycholate"
adonis_phen_df<-adonis_res.table.anno.sig[adonis_res.table.anno.sig$`SUB PATHWAY`==pheno,]$Species %>%
str_replace_all(.,"\\:\\d+_\\d+.*", "") %>%
table %>%
as.data.frame
colnames(adonis_phen_df)<-c("Species", "Count")
adonis_phen_df<-data.frame(Fecal_metabolite = rep(pheno,nrow(adonis_phen_df)), adonis_phen_df)
adonis_fmetab_count<-rbind(adonis_fmetab_count,adonis_phen_df)
}
adonis_fmetab_count <- adonis_fmetab_count[order(adonis_fmetab_count$Count),]
adonis_fmetab_count_species_order<-adonis_fmetab_count %>% group_by(Species) %>% summarise(sum(Count)) %>% .[order(.$`sum(Count)`, decreasing = T),] %>% .$Species %>% as.character
adonis_fmetab_count_fmetab_order<-adonis_fmetab_count %>% group_by(Fecal_metabolite) %>% summarise(sum(Count)) %>% .[order(.$`sum(Count)`, decreasing = T),] %>% .$Fecal_metabolite %>% as.character
pdf("06.Species_level_assoc/adonis_fmetab.circos.pdf", width = 40, height = 40)
circos.par(start.degree = 0) # ,xaxis.clock.wise = F , "clock.wise" = F
chordDiagram(adonis_fmetab_count,annotationTrack = "grid",
grid.col = c(rev(wes_palette("Darjeeling1", length(adonis_fmetab_count_fmetab_order), type = "continuous")),
rep('grey',length(adonis_fmetab_count_species_order))),
order = c(adonis_fmetab_count_fmetab_order,
rev(adonis_fmetab_count_species_order)),
big.gap = 5,
preAllocateTracks = list(track.margin = c(0, uh(150, "mm")),
track.height = max(strwidth(unlist(dimnames(adonis_fmetab_count)))))
)
circos.track(track.index = 1, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter, CELL_META$ylim[1], CELL_META$sector.index,cex = 2.5,
facing = "clockwise", niceFacing = TRUE, adj = c(0, 0.5))
}, bg.border = NA) # here set bg.border to NA is important
dev.off()
circos.clear()
## Example
species_short_name<-"F.prausnitzii"
species_dis<-all_msv_dist_std[[paste("msv_",info$organism[match(species_short_name,info$Short_name)],sep = "")]]
ba_name<-"N_acetylglutamate"
ba_vec<-all_fmetab[ba_name] %>% na.omit
species_ba_inter<-intersect(rownames(species_dis),rownames(ba_vec))
species_dis_input<-species_dis[match(species_ba_inter,rownames(species_dis)),
match(species_ba_inter, colnames(species_dis))]
ba_vec_input<-ba_vec[match(species_ba_inter, rownames(ba_vec)),]
species_dis_input_mds<-cmdscale(species_dis_input, k=5, eig = T)
species_dis_input.pcoa <- data.frame(species_dis_input_mds$points)
p_spe_ba_pcoa<-ggplot(species_dis_input.pcoa,aes(X1,X2, color = qtrans(ba_vec_input)))+
geom_point(size = 2,alpha = 0.8)+
xlab(paste("PCo1=",round(species_dis_input_mds$eig[1],digits = 2),"%",sep = ""))+
ylab(paste("PCo2=",round(species_dis_input_mds$eig[2],digits = 2),"%",sep = ""))+
scale_color_distiller(palette = "Spectral")+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line.x = element_line(),
axis.line.y = element_line(),
legend.position = 'right',
legend.title = element_blank(),
legend.key = element_rect(fill = NA),
panel.grid.major = element_line(colour = NA),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("06.Species_level_assoc/F.prausnitziiN_acetylglutamate.genetics.pdf",width = 4, height = 3)
print(p_spe_ba_pcoa)
dev.off()
##
species_short_name<-"B.wexlerae"
species_dis<-all_msv_dist_std[[paste("msv_",info$organism[match(species_short_name,info$Short_name)],sep = "")]]
ba_name<-"deoxycholate"
ba_vec<-all_fmetab[ba_name] %>% na.omit
species_ba_inter<-intersect(rownames(species_dis),rownames(ba_vec))
species_dis_input<-species_dis[match(species_ba_inter,rownames(species_dis)),
match(species_ba_inter, colnames(species_dis))]
ba_vec_input<-ba_vec[match(species_ba_inter, rownames(ba_vec)),]
species_dis_input_mds<-cmdscale(species_dis_input, k=5, eig = T)
species_dis_input.pcoa <- data.frame(species_dis_input_mds$points)
p_spe_ba_pcoa<-ggplot(species_dis_input.pcoa,aes(X1,X2, color = qtrans(ba_vec_input)))+
geom_point(size = 2,alpha = 0.8)+
xlab(paste("PCo1=",round(species_dis_input_mds$eig[1],digits = 2),"%",sep = ""))+
ylab(paste("PCo2=",round(species_dis_input_mds$eig[2],digits = 2),"%",sep = ""))+
scale_color_distiller(palette = "Spectral")+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line.x = element_line(),
axis.line.y = element_line(),
legend.position = 'right',
legend.title = element_blank(),
legend.key = element_rect(fill = NA),
panel.grid.major = element_line(colour = NA),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
pdf("06.Species_level_assoc/B.wexlerae_deoxycholate.genetics.pdf",width = 4, height = 3)
print(p_spe_ba_pcoa)
dev.off()
```
### 4.5 Species abundance and fecal metabolites