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s05_Microbiome_population_structure_France.R
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### Microbiome population structure
### 2023-09-18
### LiuRong
setwd("D:/analysis_lab/pharmacomicrobiomics/1_ICB_metagenome_SV/pipeline/4_R")
## 1 Preparation
### 1.1 Import
##install.packages("promises")
library("promises")
source("functions.R")
### 1.2 Inputs
load("01.cleanData/SV_all/France_all_shared_sv_dis.RData")
load("01.cleanData/SV_all/distMat/France_all_msv_dist_std.RData")
all_abun_sv <- read.table("01.cleanData/mbio_all/France_SV_species_abun.tsv",check.names = F)
all_basic <- read.table("01.cleanData/phen/France_Clinical_basic_overlap.tsv")
info<- read.table("01.cleanData/SV_info/France_Informative_species_information_final.tsv",
sep = "\t", header = T, stringsAsFactors = F)
## 2 Microbiome PCoA
### 2.1 SV PCoA
## based on shared sv
all_msv_dist_std_avg<-all_shared_sv_dis
all_msv_dist_std_avg_rmna<-dist_rmna(all_msv_dist_std_avg)
all_basic_sv_rmna <- all_basic[match(rownames(all_msv_dist_std_avg_rmna), rownames(all_basic)),]
all_sv_avg_dist_mds<-cmdscale(all_msv_dist_std_avg_rmna, k=5, eig = T)
all_sv_avg_dist_pcoa <- data.frame(all_sv_avg_dist_mds$points)
varExp <- (eigenvals(all_sv_avg_dist_mds)/sum(eigenvals(all_sv_avg_dist_mds)))[1:2]
xVar <- as.numeric(varExp[1]*100)
yVar <- as.numeric(varExp[2]*100)
#adonis_test<-adonis2(as.dist(all_msv_dist_std_avg_rmna) ~all_basic_sv_rmna$study, permutations = 999)
#pic_adonis <- paste0("adonis R2: ",round(adonis_test$R2,2), "; P-value: ", adonis_test$'Pr(>F)')
p_all_sv_pcoa<-ggplot(all_sv_avg_dist_pcoa,aes(X1,X2, color = all_basic_sv_rmna$dataset))+
geom_point(size = 2,alpha = 0.8)+ggtitle("France")+
stat_ellipse(aes(group = all_basic_sv_rmna$dataset, fill = all_basic_sv_rmna$dataset, color = all_basic_sv_rmna$dataset) ,type = "norm",linetype = 2,geom = "polygon",alpha = 0.05,show.legend = F)+
xlab(paste("PCo1=",round(xVar,digits = 2),"%",sep = ""))+
ylab(paste("PCo2=",round(yVar,digits = 2),"%",sep = ""))+
scale_color_manual(name=NULL,
breaks = c("PRJEB22863", "PRJNA751792"),
labels = c("RoutyB_2018", "DerosaL_2022"),
values = c("#BC3C29FF","#6F99ADFF"))+
#guides(col = guide_legend(nrow = 3, byrow = TRUE))+
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 = 'bottom',
legend.title = element_blank(),
legend.text = element_text(size =9,family ="sans",color="black"),
legend.key = element_rect(fill = NA),
axis.text.x = element_text(size = 10,family ="sans",color="black"),
axis.text.y = element_text(size = 10,family ="sans",color="black"),
panel.grid.major = element_line(colour = NA),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
p_all_sv_pcoa<-ggExtra::ggMarginal(p_all_sv_pcoa, type = "histogram", groupColour = F, groupFill = TRUE,
xparams = list(bins = 60, alpha = 0.8,position = 'identity', color = 'white'),
yparams = list(bins = 60, alpha = 0.8,position = 'identity', color = 'white'))
if(!dir.exists("05.Microbiome_population_structure")){dir.create("05.Microbiome_population_structure")}
#pdf("05.Microbiome_population_structure/all_SV_PCoA.pdf", width = 4.5, height = 5)
#print(p_all_sv_pcoa)
#dev.off()
tiff(file = "pics/F3C_France_all_SV_PCoA.tiff", width =3000, height =3000, res =600)
print(p_all_sv_pcoa)
dev.off()
### 方差分析
library(gplots)
library(multcomp)
all_sv_avg<-cbind(all_basic_sv_rmna,all_sv_avg_dist_pcoa)
aggregate(all_sv_avg$X1,by=list(all_sv_avg$study),FUN=mean)
fit <- aov(X1 ~study,data=all_sv_avg)
summary(fit)
TukeyHSD(fit)
plotmeans(all_sv_avg$X1 ~ all_sv_avg$study,
xlab = "Study",
ylab = "PC1",
main = "Mean Plot \nwith 95% CI")
aggregate(all_sv_avg$X2,by=list(all_sv_avg$study),FUN=mean)
fit <- aov(X2 ~study,data=all_sv_avg)
summary(fit)
TukeyHSD(fit)
plotmeans(all_sv_avg$X2 ~ all_sv_avg$study,
xlab = "Study",
ylab = "PC2",
main = "Mean Plot \nwith 95% CI")
################################# 2.2 Abundance PCoA
all_abun_dist <- as.matrix(vegdist(na.omit(as.data.frame(all_abun_sv)),method = "canberra"))
all_basic_abun_rmna <- all_basic[match(rownames(all_abun_dist), rownames(all_basic)),]
all_abun_dist_mds<-cmdscale(all_abun_dist, k=5, eig = T)
all_abun_dist_pcoa <- data.frame(all_abun_dist_mds$points)
varExp <- (eigenvals(all_abun_dist_mds)/sum(eigenvals(all_abun_dist_mds)))[1:2]
xVar <- as.numeric(varExp[1]*100)
yVar <- as.numeric(varExp[2]*100)
p_all_abun_pcoa<-ggplot(all_abun_dist_pcoa,aes(X1,X2, color = all_basic_abun_rmna$dataset))+
geom_point(size = 2,alpha = 0.5)+
stat_ellipse(aes(group = all_basic_abun_rmna$dataset,
fill = all_basic_abun_rmna$dataset,
color = all_basic_abun_rmna$dataset),
type = "norm",linetype = 2,geom = "polygon",alpha = 0.05,show.legend = F)+
xlab(paste("PCo1=",round(xVar,digits = 2),"%",sep = ""))+
ylab(paste("PCo2=",round(yVar,digits = 2),"%",sep = ""))+
scale_color_manual(name=NULL,
breaks = c("PRJEB22863","PRJNA751792"),
labels = c("RoutyB_2018", "DerosaL_2022"),
values = mycolor8)+
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 = 'bottom',
legend.title = element_blank(),
legend.text = element_text(size =10,family ="sans",color="black"),
legend.key = element_rect(fill = NA),
axis.text.x = element_text(size = 10,family ="sans",color="black"),
axis.text.y = element_text(size = 10,family ="sans",color="black"),
panel.grid.major = element_line(colour = NA),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
p_all_abun_pcoa<-ggExtra::ggMarginal(p_all_abun_pcoa, type = "histogram", groupColour = F, groupFill = TRUE,
xparams = list(bins = 60, alpha = 0.5,position = 'identity', color = 'white'),
yparams = list(bins = 60, alpha = 0.5,position = 'identity', color = 'white'))
#pdf("05.Microbiome_population_structure/all_abun_sv_PCoA.pdf", width = 4.5, height = 5)
#print(p_all_abun_pcoa)
#dev.off()
tiff(file = "pics/FS2_France_all_abun_PCoA.tiff", width =3000, height =3000, res =600)
print(p_all_abun_pcoa)
dev.off()
### 2.3 Effect of abundance on population structure of SV
all_sv_abun_inter<-intersect(rownames(all_basic_sv_rmna), rownames(all_basic_abun_rmna))
all_inter_basic_rmna<-all_basic[match(all_sv_abun_inter, rownames(all_basic)),]
all_inter_sv_pc <- all_sv_avg_dist_pcoa[match(all_sv_abun_inter,rownames(all_sv_avg_dist_pcoa)),]
all_inter_abun_pc <- all_sv_avg_dist_pcoa[match(all_sv_abun_inter,rownames(all_abun_dist_pcoa)),]
all_inter_sv_abun_pc<-cbind(all_inter_sv_pc, all_inter_abun_pc)
colnames(all_inter_sv_abun_pc)<-c("SV_PC1", "SV_PC2", "SV_PC3", "SV_PC4", "SV_PC5",
"Abun_PC1", "Abun_PC2", "Abun_PC3", "Abun_PC4", "Abun_PC5")
## Linear model: Remove confounding effect of abundance
# SV PC1
all_sv_pc1_abun_pc_lm.res<-lm(SV_PC1~Abun_PC1+Abun_PC2+Abun_PC3+Abun_PC4+Abun_PC5, data = all_inter_sv_abun_pc)
Abun_PC1_resi<-all_sv_pc1_abun_pc_lm.res$residuals
bartlett.test(Abun_PC1_resi~all_inter_basic_rmna$study[match(names(all_sv_pc1_abun_pc_lm.res$residuals), rownames(all_inter_basic_rmna))]
,data=all_inter_sv_abun_pc)
aov_test<-aov(Abun_PC1_resi~all_inter_basic_rmna$study[match(names(all_sv_pc1_abun_pc_lm.res$residuals), rownames(all_inter_basic_rmna))]
,data=all_inter_sv_abun_pc)
summary(aov_test)
##wilcox.test(Abun_PC1_resi~all_inter_basic_rmna$study[match(names(all_sv_pc1_abun_pc_lm.res$residuals), rownames(all_inter_basic_rmna))]) # p-value = 0.01357
# SV PC2
all_sv_pc2_abun_pc_lm.res<-lm(SV_PC2~Abun_PC1+Abun_PC2+Abun_PC3+Abun_PC4+Abun_PC5, data = all_inter_sv_abun_pc)
Abun_PC2_resi<-all_sv_pc2_abun_pc_lm.res$residuals
bartlett.test(Abun_PC2_resi~all_inter_basic_rmna$study[match(names(all_sv_pc2_abun_pc_lm.res$residuals), rownames(all_inter_basic_rmna))]
,data=all_inter_sv_abun_pc)
aov_test<-aov(Abun_PC2_resi~all_inter_basic_rmna$study[match(names(all_sv_pc2_abun_pc_lm.res$residuals), rownames(all_inter_basic_rmna))]
,data=all_inter_sv_abun_pc)
summary(aov_test)
##wilcox.test(Abun_PC2_resi~all_inter_basic_rmna$Cohort[match(names(all_sv_pc2_abun_pc_lm.res$residuals), rownames(all_inter_basic_rmna))]) # p-value = 1.826e-05
## PERMANOVA
all_sv_dist_abun_inter<-intersect(rownames(all_msv_dist_std_avg_rmna), rownames(all_abun_dist_pcoa))
all_inter_sv_dist<-all_msv_dist_std_avg_rmna[match(all_sv_dist_abun_inter,rownames(all_msv_dist_std_avg_rmna)),
match(all_sv_dist_abun_inter,colnames(all_msv_dist_std_avg_rmna))]
all_inter_abun_pc<-all_abun_dist_pcoa[match(all_sv_dist_abun_inter, rownames(all_abun_dist_pcoa)),]
all_inter_basic<-all_basic[match(all_sv_dist_abun_inter, rownames(all_basic)),]
all_inter_abun_pc_basic<-cbind(all_inter_abun_pc,all_inter_basic[,c("gender_num","age_bins","study")])
##colnames(all_inter_abun_pc_basic)[6]<-"gender" （自变量中不允许出现NA）
all_sv_dist.adonis<-adonis(as.dist(all_inter_sv_dist)~X1+X2+X3+X4+X5+gender_num+age_bins+study, data = all_inter_abun_pc_basic)
save(all_sv_dist.adonis, file = "05.Microbiome_population_structure/France_all_sv_dist.adonis.RData")
############ 3 Effect of basic phenotypes
### 3.1 PERMANOVA
all_basic_sv_p_adonis.df<-as.data.frame(matrix(NA, nrow = 8, ncol = 5))
demo_factors<-c("gender_num","age_bins","study","X4","X5","X2","X3","X1")
for (i in 1:8) {
#i<-1
all_basic_sv_p.adonis.i<-adonis(
as.dist(all_inter_sv_dist)~., #all_msv_dist_std_avg_rmna
data = as.data.frame(all_inter_abun_pc_basic[,demo_factors[i]])) #as.data.frame(all_basic_sv_rmna[,demo_factors[i]])
all_basic_sv_p_adonis.df[i,c(1:3)]<-c(demo_factors[i],
all_basic_sv_p.adonis.i$aov.tab$R2[1],
all_basic_sv_p.adonis.i$aov.tab$`Pr(>F)`[1])
}
all_basic_sv_p.adonis.comb<-adonis(as.dist(all_inter_sv_dist)~gender_num+age_bins+study+X4+X5+X2+X3+X1,data = all_inter_abun_pc_basic)
all_basic_sv_p.adonis.comb.mat<-all_basic_sv_p.adonis.comb$aov.tab[c(1:8),] %>%
.[match(demo_factors, rownames(.)),]
all_basic_sv_p_adonis.df[,4]<-all_basic_sv_p.adonis.comb.mat$R2
all_basic_sv_p_adonis.df[,5]<-all_basic_sv_p.adonis.comb.mat$`Pr(>F)`
colnames(all_basic_sv_p_adonis.df)<-c("Factor",
"Individual_R2", "Individual_p",
"Combined_R2", "Combined_p")
all_basic_sv_p_adonis.df$Cumulative_R2<-cumsum(all_basic_sv_p_adonis.df$Combined_R2)
all_basic_sv_p_adonis.df$Factor[1:8]<-c( "Gender","Age","Cohort","abun_PC4","abun_PC5","abun_PC2","abun_PC3","abun_PC1")
write.table(all_basic_sv_p_adonis.df, "05.Microbiome_population_structure/all_basic_sv_p_adonis.tsv",
sep = "\t", col.names = T, row.names = F, quote = F)
save(all_basic_sv_p_adonis.df, file = "05.Microbiome_population_structure/France_all_basic_sv_p_adonis.df.RData")
### 3.2 Visulization
demo_factors<-c("Gender","Age","Cohort","abun_PC4","abun_PC5", "abun_PC2","abun_PC3","abun_PC1")
## ProPortion
load("05.Microbiome_population_structure/France_all_basic_sv_p_adonis.df.RData")
knitr::kable(all_basic_sv_p_adonis.df)
all_basic_sv_p_adonis.df.long<-gather(all_basic_sv_p_adonis.df[, c(1,2,6)], 'R2', 'Value', -1)
all_basic_sv_p_adonis.df.long$Value<-as.numeric(all_basic_sv_p_adonis.df.long$Value)
all_basic_sv_p_adonis.df.long$R2 <- factor(all_basic_sv_p_adonis.df.long$R2,
levels = c("Individual_R2", "Cumulative_R2"))
p_all_basic_sv_p_adonis<-ggplot(all_basic_sv_p_adonis.df.long, aes(Factor, Value,group = R2))+
geom_bar(aes(fill = R2),position =position_dodge(),stat="identity")+
xlab(NULL)+
ylab(bquote("R"^2))+
ggtitle("France")+
scale_x_discrete(limits = demo_factors)+
scale_y_continuous(expand = c(0,0))+
scale_fill_manual(name=NULL,
breaks = c("Individual_R2", "Cumulative_R2"),
labels = c(bquote("Univariate R"^2),bquote("Cumulative R"^2) ),
values = mycolor2_green_blue)+
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
plot.margin=unit(c(2,1,1,2),"cm"),plot.title = element_text(size = 20),
axis.line.x = element_line(),
axis.line.y = element_line(),
axis.title.y= element_text(size=20),
axis.text.x = element_text(angle = 45, hjust = 1,size=20),
axis.text.y = element_text(size=20),
legend.position="top",
legend.key = element_rect(fill = NA),
legend.text = element_text(size = 20),
panel.grid.major = element_line(colour = NA,linetype = "dashed",size = 0.25),
panel.grid.minor = element_line(colour = NA),
panel.background = element_rect(fill = NA))
#pdf("05.Microbiome_population_structure/basic_sv_adonis_bar.pdf", width = 5.5, height = 4)
#print(p_all_basic_sv_p_adonis)
#dev.off()
tiff(file = "pics/FS1_France_basic_sv_adonis_bar.tiff", width =2200, height =2200, res =300)
print(p_all_basic_sv_p_adonis)
dev.off()
## 4 Species-level PCoA
all_msv_dist_std<-msv_dist_std
msv_pc_cum0.6<-NA
msv_pc_cum0.6_info<-data.frame(matrix(NA, nrow = nrow(info), ncol = 3))
for (i in c(1:nrow(info))) {
# i <- 1
msv_pc_res_i<-get_PCs(all_msv_dist_std[[i]])
msv_pc_i<-msv_pc_res_i$PCoA
msv_pc_i<-msv_pc_i[match(rownames(all_basic), rownames(msv_pc_i)),]
rownames(msv_pc_i) <- rownames(all_basic)
colnames(msv_pc_i)<-paste(info$organism[i],"_PC", c(1:ncol(msv_pc_i)),sep = "")
msv_pc_cum0.6<-cbind(msv_pc_cum0.6, msv_pc_i)
msv_pc_cum0.6_info[i,]<-c(info$organism[i], msv_pc_res_i$PC_num, msv_pc_res_i$Total_eig)
}
msv_pc_cum0.6<-msv_pc_cum0.6[,-1]
## all
write.table(msv_pc_cum0.6, "01.cleanData/SV_all/France_all_msv_pc_cum0.6.tsv",sep = "\t", quote = F)
save(msv_pc_cum0.6, file = "01.cleanData/SV_all/France_msv_pc_cum0.6.RData")
## PC information
colnames(msv_pc_cum0.6_info)<-c("Species", "PC_number", "Explained_variance_proportion")
write.table(msv_pc_cum0.6_info, "01.cleanData/SV_all/France_all_msv_pc_cum0.6_info.tsv",col.names = T, row.names = F,sep = "\t", quote = F)
save(msv_pc_cum0.6_info, file = "01.cleanData/SV_all/France_msv_pc_cum0.6_info.RData")