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Copy pathDEG_Anno_Plot.R
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DEG_Anno_Plot.R
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#!/usr/bin/Rscript
#options(echo = TRUE)
httr::set_config(httr::config(ssl_verifypeer = FALSE))
args = commandArgs(trailingOnly=TRUE) # Passing arguments to an R script from bash/shell command lines
# make folder DEG to store outputs
setwd(dirname(args[1]))
#system("mkdir -p DEG") # make new a folder DEG in current working directory
library(DESeq2)
library(tibble)
# Read & preprocess the input file cts before run deseq2 analysis
filename<-basename(args[1])
if (filename %in% c("humann_genefamilies_Abundance_go_translated.tsv","humann_genefamilies_Abundance_kegg_translated.tsv")){
# read the data file
#translated file has additional quote symbol "\""
cts<-read.table(args[1],
row.names=1, sep="\t",header=T)
#round the values before as.integer
cts<-as.data.frame(lapply(lapply(cts,round),as.integer),row.names = row.names(cts))
# make a folder for outputs
if (filename == "humann_genefamilies_Abundance_go_translated.tsv"){
dir.create("Nonhost_hmn_DEG/GO",recursive = T)
setwd("Nonhost_hmn_DEG/GO")
}
if (filename == "humann_genefamilies_Abundance_kegg_translated.tsv"){
dir.create("Nonhost_hmn_DEG/KEGG",recursive = T)
setwd("Nonhost_hmn_DEG/KEGG")
}
}
if (filename %in% c("bracken_species_all","bracken_phylum_all","bracken_genus_all")){
# read the data file
#bracken file (eg. bracken_species_all) without quote symbol "\""; mark empty quote as quote=""
cts<-read.table(args[1],
row.names=1, sep="\t",header=T, quote="")
# Extract columns with count numbers
cts<-cts[,grepl("*_num", names(cts))]
# match column name to sample name
#cts <- cts[, grepl("*_num", names(cts))] # Filtra as colunas de cts
features <- cts # Aqui, features pode ser definido como cts ou um subconjunto específico de cts
print(dim(cts))
print(dim(features))
colnames(cts)<-gsub("^Report_|\\.species.bracken_num$|\\.phylum.bracken_num$|.genus.bracken_num$","",colnames(cts))
# decontamination step
# read a list of contamination organisms
# conta_ls<-read.table("~/Dual-seq/conta_ls.txt",sep="\t",colClasses = c("character","NULL"))
# conta_ls<-conta_ls[conta_ls != "",]
# remove the entries matching the contamination organisms
# for (c in conta_ls){
# cts<-cts[!grepl(c,row.names(cts)),]
# }
# make a folder for outputs
dir.create("Nonhost_DEG", recursive = T)
setwd("Nonhost_DEG")
# save the decontaminated result for later reference
# write.table(cts,paste0(filename,"_decontaminated"),sep="\t", quote = F, col.names = NA)
}
if (filename == "host_counts.txt"){
# read the data file
#featureCounts file (eg. host_counts.txt) without quote symbol "\""; mark empty quote as quote=""
cts<-read.table(args[1],
row.names=1, sep="\t",header=T, quote="")
# drop first 5 columns with information other than counts
cts<-cts[,-c(1:5)]
# drop rows of zero count
cts<-cts[rowSums(cts[-1])>0,]
# make a folder for outputs
dir.create("Host_DEG", recursive = T)
setwd("Host_DEG")
}
# read the original samplesheet
coldata0 <- read.csv(args[2], header = T, na.strings=c("","NA"))
# extract the contrast information for reference
coldata_vs<- coldata0[c("group1","group2")]
coldata_vs<-coldata_vs[rowSums(is.na(coldata_vs)) == 0,] #remove the NA rows
# read samplesheet as factors (as.is = F) for Deseq2 statistical analysis
coldata_factor <- read.csv(args[2], header = T, as.is = F)
coldata_factor[]<-lapply(coldata_factor, factor)
coldata<-coldata_factor[,1:2]
# update the coldata if metadata is provided
if (length(args) == 5){
coldata <- read.csv(args[5], header = T, as.is = F)
coldata[]<-lapply(coldata, factor)
}
if (filename == "host_counts.txt"){
# make cts(count matrix) has consistent order with samplesheet/metadata
cts<-cts[coldata0$sample_name]
# load the datastructure to DESeq
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = coldata,
design= ~ group)
}
if (filename %in% c("bracken_species_all","bracken_phylum_all","bracken_genus_all",
"humann_genefamilies_Abundance_go_translated.tsv","humann_genefamilies_Abundance_kegg_translated.tsv")){
if (filename %in% c("bracken_species_all","bracken_phylum_all","bracken_genus_all")){
files_h <- list.files(path=paste0(dirname(args[1]),"/temp"), pattern="^Report_host_.*\\.txt$", full.names=TRUE, recursive=FALSE)
}
if (filename %in% c("humann_genefamilies_Abundance_go_translated.tsv","humann_genefamilies_Abundance_kegg_translated.tsv")){
humann_f <- gsub("/hmn_genefamily_abundance_files","",dirname(args[1]))
files_h <- list.files(path=paste0(humann_f,"/temp"), pattern="^Report_host_.*\\.txt$", full.names=TRUE, recursive=FALSE)
}
# to get a list for host
transcriptome_size<-c() #generate a empty list
for (i in files_h){
t<-read.table(i,sep="\t", quote= "")
total_reads<-t[1,2] + t[2,2] #get total reads abundance of a sample
fn<-gsub("^Report_host_|\\.txt$","",basename(i)) #grep the sample name
transcriptome_size<-c(transcriptome_size,setNames(total_reads,fn)) #add sample name with value to the list lh
}
transcriptome_size <- log2(transcriptome_size)-mean(log2(transcriptome_size))
coldata$order<-1:nrow(coldata)
coldata<-merge(coldata,as.data.frame(transcriptome_size), by.x="sample_name",by.y="row.names")
coldata<-coldata[order(coldata$order), ]
coldata<-subset(coldata, select = -c(order))
# make cts(count matrix) has consistent order with samplesheet/metadata
cts<-cts[coldata0$sample_name]
# load the datastructure to DESeq
dds <- DESeqDataSetFromMatrix(countData = cts,
colData = coldata,
design= ~ group + transcriptome_size)
}
# adjust the design if metadata is provided
funNew <- function(x){
as.formula(paste("~", paste(x, collapse = " + ")))
}
if (length(args) == 5){
design(dds)<-funNew(names(coldata)[2:ncol(coldata)])
}
# perform the DESeq analysis
dds <- DESeq(dds)
# Data transformation for visualization (normalization included)
#rld<-rlog(dds,blind=F) # regularized log transformation (log2 based)
if (dim(results(dds))[1] < 1000 || min(colSums(cts !=0)) < 1000){
vsd<-varianceStabilizingTransformation(dds,blind=F) # vatiance stabilizing transformation
} else {
vsd<-vst(dds,blind=F)
}
normtrans<-assay(vsd)
# save normalized & transformed data for visualization
write.csv(normtrans,file=paste0(sub(".tsv$|.txt$","",filename),"_normalized_transformed.csv"))
# save normalized (untransformed) data for reference
norm<-counts(dds,normalized=T)
write.csv(norm,file=paste0(sub(".tsv$|.txt$","",filename),"_normalized.csv"))
# merge and add suffixes; normalized and normalized&transformed
merge.nt<-merge(norm,normtrans,by="row.names", suffixes=c(".norm",".normtrans"))
if (filename == "host_counts.txt"){
library("biomaRt")
host_sp<-read.csv(args[4]) # read a list of supported host species
# add annotations; try up to 120 times/20 mins if biomaRt server not response
ensembl <- NULL
attempt <- 0
while ( is.null(ensembl) && attempt <= 120){
try(
ensembl <- useMart("ensembl",dataset=host_sp[host_sp$Taxon_ID==args[3],2])
)
attempt<-attempt+1
if (attempt > 1){
print(paste0("Retry to get gene annotations from ensembl: ",attempt," times"))
}
Sys.sleep(10)
}
if (is.null(ensembl)){
stop("Failed to get gene annotations from ensembl server, please try again later")
}
names(merge.nt)[1]<-"GeneID"
genes <- merge.nt$GeneID
gene_ID <- getBM(filters="ensembl_gene_id",
attributes=c("external_gene_name","ensembl_gene_id",
"chromosome_name","start_position","end_position",
"strand","gene_biotype","description"),
values=genes,mart=ensembl)
names(gene_ID)[names(gene_ID)=="external_gene_name"]<-"gene_name" #rename the first column
gene_len<-read.table(args[1], row.names=1, sep="\t",header=T, quote="")
gene_len<-gene_len["Length"]
colnames(gene_len)<-"gene_length"
gene_ID<-merge(gene_ID,gene_len,by.x="ensembl_gene_id", by.y="row.names")
}
# #to add entrezid separately; caution: may bring a issue of "duplicate" ENTREZID!!
# library(clusterProfiler)
# library(org.Mmu.eg.db)
# # may bring a issue of "duplicate" ENTREZID!!
# ENTREZID = bitr(gene_ID$external_gene_name,
# fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mmu.eg.db") #get entrezid with acceptable id duplicate
# gene_ID <- merge(gene_ID,ENTREZID,by.x="gene_name",by.y="SYMBOL",all.x = T)
# function for host
# function of contrast between groups, merge to annotation and count tables, and save to .csv files
comparison<-function(dds,coldata_vs,filename,gene_ID,cts,merge.nt){
for (i in 1:nrow(coldata_vs)){
group1<-coldata_vs$group1[i]
group2<-coldata_vs$group2[i]
comparison <- results(dds, contrast=c("group", group1, group2))
comparison_f<-as.data.frame(comparison)
comparison_f<-comparison_f[order(comparison_f$pvalue),]
system(paste0("mkdir -p"," ",group1,"_vs_",group2)) #for output file structure
setwd(paste0(group1,"_vs_",group2))
write.csv(comparison_f,file=paste0(sub(".tsv$|.txt$","",filename),"_",group1,"_vs_",group2,".csv"))
setwd("../")
colnames(comparison_f)<-paste0(colnames(comparison_f),".",group1,"_vs_",group2)
gene_ID<-merge(gene_ID,comparison_f,by.x="ensembl_gene_id", by.y="row.names") #merge each comparison to annotation
}
Anno.merge.all<-merge(cts,gene_ID,by.x="row.names",by.y="ensembl_gene_id")
Anno.merge.all<-merge(Anno.merge.all,merge.nt,by.x="Row.names",by.y="GeneID")
names(Anno.merge.all)[1]<-"gene_id" #rename the first column
Anno.merge.all[Anno.merge.all==""]<-"-" #replace the empty cells to "-"
Anno.merge.all<-Anno.merge.all[complete.cases(Anno.merge.all[,grep("pvalue|padj",names(Anno.merge.all))]),] # drop pvalue == NA rows; optional
hybrid_name<-Anno.merge.all$gene_name # add a column of gene_id/name hybrid
while ("-" %in% hybrid_name){
hybrid_name[match("-",hybrid_name)] <-
Anno.merge.all$gene_id[match("-",hybrid_name)]}
Anno.merge.all<-add_column(Anno.merge.all, hybrid_name, .after = "gene_name")
write.csv(Anno.merge.all,file=paste0(sub(".tsv$|.txt$","",filename),"_DEG.csv"),row.names=F)
}
#function for non-host
comparison_nonhost<-function(dds,coldata_vs,filename,cts,merge.nt){
gene_ID_temp<-list()
for (i in 1:nrow(coldata_vs)){
group1<-coldata_vs$group1[i]
group2<-coldata_vs$group2[i]
comparison <- results(dds, contrast=c("group", group1, group2))
comparison_f<-as.data.frame(comparison)
comparison_f<-comparison_f[order(comparison_f$pvalue),]
system(paste0("mkdir -p"," ",group1,"_vs_",group2)) #for output file structure
setwd(paste0(group1,"_vs_",group2))
write.csv(comparison_f,file=paste0(sub(".tsv$|.txt$","",filename),"_",group1,"_vs_",group2,".csv"))
setwd("../")
colnames(comparison_f)<-paste0(colnames(comparison_f),".",group1,"_vs_",group2)
comparison_f <- tibble::rownames_to_column(comparison_f, "NAME")
gene_ID_temp[[i]]<-comparison_f
}
library(dplyr)
gene_ID <- purrr::reduce(gene_ID_temp,full_join, by = "NAME") #reduce multiple data.frames in a list to a single data.frame
names(merge.nt)[1]<-"Name" #rename the first column
Anno.merge.all<-merge(cts,gene_ID,by.x="row.names", by.y="NAME")
Anno.merge.all<-merge(Anno.merge.all,merge.nt,by.x="Row.names",by.y="Name")
names(Anno.merge.all)[1]<-"Name" #rename the first column
Anno.merge.all[Anno.merge.all==""]<-"-" #replace the empty cells to "-"
Anno.merge.all<-Anno.merge.all[complete.cases(Anno.merge.all[,grep("pvalue|padj",names(Anno.merge.all))]),] # drop pvalue == NA rows
write.csv(Anno.merge.all,file=paste0(sub(".tsv$|.txt$","",filename),"_DEG.csv"),row.names=F)
}
# apply the comparison function
if (filename == "host_counts.txt"){
comparison(dds, coldata_vs, filename,gene_ID,cts,merge.nt)
} else {comparison_nonhost(dds, coldata_vs, filename,cts,merge.nt)}
#to match with sample name from DESeq2, which is not allow "-" in the name
coldata[,1] <-gsub("-",".",coldata[,1])
## MaAsLin2 ##
library("Maaslin2")
system("mkdir -p MaAsLin2_results")
features <- t(cts)
metadata <- coldata
rownames(metadata)<-metadata[,1]
metadata<-subset(metadata,select=-1)
covar<-toString(shQuote(names(coldata)[2:ncol(coldata)]))
for (r in 1:length(unique(coldata_vs[,2]))){
fit_data <- Maaslin2(features, metadata, paste0('MaAsLin2_results/ref_',unique(coldata_vs[,2])[r]),
fixed_effects = cat(covar, "\n"),
reference = paste0("group,",unique(coldata_vs[,2])[r]),
plot_heatmap = T, plot_scatter = T,
min_abundance = 0.01, # Valor mais baixo para evitar remoção de features
min_prevalence = 0.1, # Prevalência mínima baixa para reter mais features
cores=10)
}
if (filename == "host_counts.txt"){ #prepare for gene symbol
system("mkdir -p MaAsLin2_results/gene_symbol")
DEG<-read.csv(paste0(sub(".tsv$|.txt$","",filename),"_DEG.csv"),header = T)
df.DEG<-DEG[!duplicated(DEG$hybrid_name),] #remove duplicate gene symbol
df4features <- data.frame(df.DEG$hybrid_name, df.DEG[names(cts)], row.names=1)
features.symbol <- t(df4features)
for (r in 1:length(unique(coldata_vs[,2]))){
fit_data <- Maaslin2(features.symbol, metadata, paste0('MaAsLin2_results/gene_symbol/ref_',unique(coldata_vs[,2])[r]),
fixed_effects = cat(covar, "\n"),
reference = paste0("group,",unique(coldata_vs[,2])[r]),
plot_heatmap = T, plot_scatter = T,
min_abundance = 0.01, # Valor mais baixo para features simbólicas
min_prevalence = 0.1, # Prevalência mínima baixa para reter mais
cores=10)
}
write("The number of genes may be less due to the duplicated gene symbols being removed.","MaAsLin2_results/gene_symbol/Readme.txt")
}
if (filename == "host_counts.txt"){
setwd(paste0(dirname(args[1]),"/Host_DEG")) # go back to the Host_DEG folder
}
### Plots ###
library(ggplot2)
library(ggrepel)
library(dplyr)
library(stringr) #for str_trunc function: restrict the showing character length
library(colorspace)
library(RColorBrewer)
# subset normtrans for visualization; consider all groups; to integrate in the comparison function?? DEG=Anno.merge.all
DEG<-read.csv(paste0(sub(".tsv$|.txt$","",filename),"_DEG.csv"),header = T)
# count gene TPM
if (filename == "host_counts.txt"){
RPK <- DEG[,as.character(coldata[,1])]/DEG$gene_length
TPM <- RPK*1000000/sum(RPK)
TPM<-cbind(DEG[,c("gene_name","hybrid_name")],TPM)
write.csv(TPM,file=paste0(sub(".tsv$|.txt$","",filename),"_TPM.csv"),row.names=F)
}
# filter by log2 <0.5 & >0.5 and p-value <0.05 for all groups
flt_groups_all<-data.frame()
for (i in 1:length(grep("pvalue",names(DEG)))){
p<-DEG[DEG[,grep("pvalue",names(DEG))[i]]<0.05 &
abs(DEG[,grep("log2FoldChange",names(DEG))[i]])>0.5,]
flt_groups_all<-rbind(flt_groups_all,p)
}
# flt_groups<-DEG[DEG[,grep("pvalue",names(DEG))[i]]<0.05 &
# abs(DEG[,grep("log2FoldChange",names(DEG))[i]])>0.5,]
#
# flt_groups<-flt_groups[complete.cases(flt_groups),]
# flt_groups_all <- DEG[(abs(DEG[,"log2FoldChange.ART_Young_vs_Ctl_Young"]) > 0.5 |
# abs(DEG[,"log2FoldChange.ART_Old_vs_ART_Young"]) > 0.5) &
# (DEG[,"pvalue.ART_Young_vs_Ctl_Young"]<0.05 |
# DEG[,"pvalue.ART_Old_vs_ART_Young"]<0.05),]
#subset the interested columnes
subset_normtrans<-flt_groups_all[,grep("normtrans|gene_name|gene_id|Name",names(flt_groups_all))]
if (filename == "host_counts.txt"){
# pass undefined gene name
while ("-" %in% subset_normtrans$gene_name){
subset_normtrans$gene_name[match("-",subset_normtrans$gene_name)] <-
subset_normtrans$gene_id[match("-",subset_normtrans$gene_name)]
}
##check the duplicated rows
#n_occur <- data.frame(table(subset_normtrans$gene_name))
#n_occur[n_occur$Freq > 1,]
##drop the duplicated rows
#flt_groups_all<-unique(flt_groups_all)
# drop the duplicate rows
subset_normtrans<-subset_normtrans[!duplicated(subset_normtrans$gene_name),]
#convert column name (gene_name) to row name
subset_normtrans2<-subset_normtrans[,-1:-2]
rownames(subset_normtrans2)<-subset_normtrans[,2]
} else {
# drop the duplicate rows
subset_normtrans<-subset_normtrans[!duplicated(subset_normtrans$Name),]
#convert column name (gene_name) to row name
subset_normtrans2<-subset_normtrans[,-1]
rownames(subset_normtrans2)<-subset_normtrans[,1]
}
#chop the sample column names
colnames(subset_normtrans2)<-gsub(".normtrans","",colnames(subset_normtrans2))
#heatmap require matrix
transdata <- as.matrix(subset_normtrans2)
# adjust the rowname length
for (i in 1:length(row.names(transdata))){
l<-nchar(row.names(transdata)[i])
if (l > 35){
row.names(transdata)[i]<-str_trunc(row.names(transdata)[i],35)
}
}
#read the column annotation information (derive from samplesheet.csv)
Anno_col<-as.data.frame(coldata[,2])
rownames(Anno_col)<-coldata[,1]
colnames(Anno_col)<-"Group"
# annotation color selection
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
n<-nrow(unique(Anno_col))
cols = gg_color_hue(n)
names(cols)<-as.character(unique(Anno_col)$Group)
anno_colors<-list(Group=cols)
# draw heat map
library(pheatmap)
hp_thumbnail<-pheatmap(transdata, cluster_cols = T, scale="row",
annotation_col =Anno_col, annotation_colors=anno_colors,
show_rownames=F,cex=1)
if (nrow(transdata)>100){
hp<-pheatmap(transdata, cluster_cols = T, scale="row",
fontsize_row=5, annotation_col =Anno_col,
annotation_colors=anno_colors)
} else {
hp<-pheatmap(transdata, cluster_cols = T, scale="row",
annotation_col =Anno_col,
annotation_colors=anno_colors)
}
# hp<-pheatmap(transdata, cluster_cols = T, scale="row",
# fontsize_row=5,cex=1, annotation_col =Anno_col, cex=0.9)
# save the plot
ggsave("heatmap_thumbnail.pdf",plot=hp_thumbnail)
if (nrow(transdata)>100){
ggsave("heatmap.pdf",plot=hp,
limitsize = F,
height = 0.1*nrow(transdata),
width = 0.8*ncol(transdata))
} else {
ggsave("heatmap.pdf",plot=hp,height = 0.2*nrow(transdata))
}
## draw PCA
if (filename %in%
c("humann_genefamilies_Abundance_go_translated.tsv",
"humann_genefamilies_Abundance_kegg_translated.tsv",
"host_counts.txt")){
pcadata<-plotPCA(vsd,intgroup=c("group"), returnData=T)
percentVar<-round(100*attr(pcadata,"percentVar"))
ggplot(pcadata, aes(PC1, PC2, shape=group)) +
geom_point(size=3) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() +
theme_bw() +
ggtitle("PCA")
ggsave("PCA.pdf")
ggplot(pcadata, aes(PC1, PC2, color=group)) +
geom_point(size=3) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
coord_fixed() +
theme_bw() +
ggtitle("PCA")
ggsave("PCA_color.pdf")
ggplot(pcadata, aes(PC1, PC2, shape=group)) +
geom_point(size=3) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
geom_text_repel(aes(label=pcadata$name), size=3, max.overlaps = Inf, segment.size = 0.5) +
coord_fixed() +
theme_bw() +
ggtitle("PCA")
ggsave("PCA_label.pdf")
ggplot(pcadata, aes(PC1, PC2, color=group)) +
geom_point(size=3) +
xlab(paste0("PC1: ",percentVar[1],"% variance")) +
ylab(paste0("PC2: ",percentVar[2],"% variance")) +
geom_text_repel(aes(label=pcadata$name),size=3, max.overlaps = Inf) +
coord_fixed() +
theme_bw() +
ggtitle("PCA")
ggsave("PCA_label_color.pdf")
}
# draw PCoA, heatmap, diversity tests for microbiome
if (filename %in% c("bracken_species_all",
"bracken_phylum_all",
"bracken_genus_all")){
library(phyloseq)
library(vegan)
#Imported original biom file into phyloseq object
biomfilename=paste0(dirname(args[1]),"/temp/bracken_species_all0.biom")
data<-import_biom(biomfilename,parseFunction = parse_taxonomy_default)
colnames(tax_table(data)) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
#Imported host transcriptome size adjusted & normtrans biom file into phyloseq object
biomfilename.adj=paste0(dirname(args[1]),"/bracken_species_all.biom")
data.adj<-import_biom(biomfilename.adj,parseFunction = parse_taxonomy_default)
colnames(tax_table(data.adj)) <- c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species")
#Estimated and exported alpha diversity
#The measures of diversity that aren't totally reliant on singletons, eg. Shannon/Simpson, are valid to use, and users can ignore the warning in phyloseq when calculating those measures.
data.alpha<-estimate_richness(data.adj)
write.csv(data.alpha, file="alpha-diversity.csv")
theme_set(theme_bw())
p.alpha <- plot_richness(data,measures = c("Shannon","Simpson"))
p.alpha
ggsave("Alpha_diversity_sample.pdf")
#Estimated and exported beta diversity (Bray-Curtis)
#to transformation data by vst before beta diversity analysis
if (length(args) == 5){
samples4phyloseq<-as.data.frame(coldata[2:ncol(coldata)], row.names = as.character(coldata$sample_name))
sampledata<-sample_data(samples4phyloseq)
data<-merge_phyloseq(data,sampledata)
dds1 <- phyloseq_to_deseq2(data, funNew(names(coldata)[2:ncol(coldata)]))
} else {
samples4phyloseq<-as.data.frame(coldata$group, row.names = as.character(coldata$sample_name))
colnames(samples4phyloseq)<-"group"
sampledata<-sample_data(samples4phyloseq)
data<-merge_phyloseq(data,sampledata)
dds1 <- phyloseq_to_deseq2(data, ~ group)
}
## ANCOMBC ##
library("ANCOMBC")
pseq <- phyloseq::tax_glom(data, taxrank = "Species") # species taxid
pseq1 <- microbiome::aggregate_taxa(data,"Species") # species name
ancombc_out <- function(pseq,formula){
ancombc(
phyloseq = pseq,
formula = formula,
p_adj_method = "fdr",
zero_cut = 0.90, # by default prevalence filter of 10% is applied
lib_cut = 0,
group = "group",
struc_zero = TRUE,
neg_lb = TRUE,
tol = 1e-5,
max_iter = 100,
conserve = TRUE,
alpha = 0.05,
global = TRUE
)
}
if (length(args) == 5){
out <- ancombc_out(pseq,paste(names(coldata)[2:(ncol(coldata)-1)], collapse = " + "))
out1 <- ancombc_out(pseq1,paste(names(coldata)[2:(ncol(coldata)-1)], collapse = " + "))
} else {
out <- ancombc_out(pseq,"group") # taxid
out1 <- ancombc_out(pseq1,"group") # species name
}
res <- out$res # taxid
res_global = out$res_global
res1 <- out1$res # species name
res_global1 = out1$res_global
system("mkdir -p ANCOMBC_results")
write.csv(res[["diff_abn"]], file="ANCOMBC_results/diff_abundance.csv")
write.csv(res[["W"]], file="ANCOMBC_results/Test_statistics.csv")
write.csv(res[["p_val"]], file="ANCOMBC_results/p_value.csv")
write.csv(res[["q_val"]], file="ANCOMBC_results/q_value.csv")
write.csv(res_global, file="ANCOMBC_results/Global_test.csv")
system("mkdir -p ANCOMBC_results/with_species_names")
write.csv(res1[["diff_abn"]], file="ANCOMBC_results/with_species_names/diff_abundance_name.csv")
write.csv(res1[["W"]], file="ANCOMBC_results/with_species_names/Test_statistics_name.csv")
write.csv(res1[["p_val"]], file="ANCOMBC_results/with_species_names/p_value_name.csv")
write.csv(res1[["q_val"]], file="ANCOMBC_results/with_species_names/q_value_name.csv")
write.csv(res_global1, file="ANCOMBC_results/with_species_names/Global_test_name.csv")
## heatmap for ANCOMBC results ##
ANCOMBC_plot <- function(res){
library(tidyr)
heat.mw <- res[["W"]] %>%
rownames_to_column("taxid") %>% #preserve rownames
gather(key, value, -taxid)
heat.md <- res[["diff_abn"]] %>%
rownames_to_column("taxid") %>% #preserve rownames
gather(key, value, -taxid)
heat.m<-merge(heat.mw,heat.md,by="taxid")
# Arrange the figure
p <- ggplot(heat.m, aes(x = key.x, y = taxid, fill = value.x))
p <- p + geom_tile()
p <- p + scale_fill_gradientn("value.x", name ="Test statistics",
breaks = seq(from = -2, to = 2, by = 0.5),
colours = c("darkblue", "blue", "white", "red", "darkred"),
limits = c(-2,2))
# Polish texts
p <- p + theme(axis.text.x=element_text(angle = 90, hjust=1, face = "italic"),
axis.text.y=element_text(size = 8))
p <- p + xlab("") + ylab("Taxonomy ID")
# Mark the most significant cells with stars
if (length(unique(heat.m$taxid))>100){
p <- p + geom_text(data = subset(heat.m, value.y == "TRUE"),
aes(x = key.x, y = taxid, label = "+"))
} else {
p <- p + geom_text(data = subset(heat.m, value.y == "TRUE"),
aes(x = key.x, y = taxid, label = "+"), col = "white", size = 3)
}
if (length(unique(heat.m$taxid))>100){
ggsave("heatmap_ANCOMBC.pdf",plot=p,
limitsize = F,
height = 0.1*length(unique(heat.m$taxid)),
width = 1.5+0.6*length(unique(heat.m$key.x)))
} else {
ggsave("heatmap_ANCOMBC.pdf",plot=p,height = 0.2*length(unique(heat.m$taxid)))
}
}
setwd("ANCOMBC_results")
ANCOMBC_plot(res)
ANCOMBC_plot_sp <- function(res1){
library(tidyr)
heat.mw <- res1[["W"]] %>%
rownames_to_column("name") %>% #preserve rownames
gather(key, value, -name)
heat.md <- res1[["diff_abn"]] %>%
rownames_to_column("name") %>% #preserve rownames
gather(key, value, -name)
heat.m<-merge(heat.mw,heat.md,by="name")
heat.m$name <- sub(".*s__", "", heat.m$name)
heat.m$name <- stringr::str_trunc(heat.m$name, 31) # truncate sp_name
# Arrange the figure
p <- ggplot(heat.m, aes(x = key.x, y = name, fill = value.x))
p <- p + geom_tile()
p <- p + scale_fill_gradientn("value.x", name ="Test statistics",
breaks = seq(from = -2, to = 2, by = 0.5),
colours = c("darkblue", "blue", "white", "red", "darkred"),
limits = c(-2,2))
# Polish texts
p <- p + theme(axis.text.x=element_text(angle = 90, hjust=1, face = "italic"),
axis.text.y=element_text(size = 8))
p <- p + xlab("") + ylab("Species name")
# Mark the most significant cells with stars
if (length(unique(heat.m$name))>100){
p <- p + geom_text(data = subset(heat.m, value.y == "TRUE"),
aes(x = key.x, y = name, label = "+"), col = "white", size = 2.5)
} else {
p <- p + geom_text(data = subset(heat.m, value.y == "TRUE"),
aes(x = key.x, y = name, label = "+"), col = "white", size = 3)
}
if (length(unique(heat.m$name))>100){
ggsave("heatmap_ANCOMBC.pdf",plot=p,
limitsize = F,
height = 0.1*length(unique(heat.m$name)),
width = 2.5+0.8*length(unique(heat.m$key.x)))
} else {
ggsave("heatmap_ANCOMBC.pdf",plot=p,height = 0.2*length(unique(heat.m$name)))
}
}
setwd("with_species_names")
ANCOMBC_plot_sp(res1)
setwd("../..")
# continue for beta diversity
data1<-data
vsd1 <- varianceStabilizingTransformation(dds1)
# normalized reads count with host transcriptome size and with avoiding removing variation associated with the other conditions
if (length(args) == 5){
mm <- model.matrix(funNew(names(coldata)[2:(ncol(coldata)-1)]), colData(vsd))
} else {
mm <- model.matrix(funNew(names(coldata)[2]), colData(vsd))
}
vsd1.df <- limma::removeBatchEffect(assay(vsd1), vsd$transcriptome_size, design=mm)
vsd1.df[vsd1.df < 0.0] <- 0.0 #adjust negative values after vst
otu_table(data1) <- otu_table(vsd1.df, taxa_are_rows = TRUE)
braycurtis <- phyloseq::distance(data1, method = "bray")
BCmat <- as.matrix(braycurtis)
write.csv(BCmat, file = "braycurtis.csv")
#Created and exported PCoA values
braycurtis.pcoa <- ordinate(data1, method = "PCoA", distance = "bray")
braycurtis.pcoa.export <- as.data.frame(braycurtis.pcoa$vectors, row.names = NULL, optional = FALSE, cut.names = FALSE, col.names = names(braycurtis.pcoa$vectors), fix.empty.names = TRUE, stringsAsFactors = default.stringsAsFactors())
write.csv(braycurtis.pcoa.export, file="braycurtis-pcoa.csv")
# add the group information in coldata
coldata_order<-coldata # to keep the original order as the samplesheet
coldata_order$order<-1:nrow(coldata_order)
braycurtis.pcoa.export<-merge(braycurtis.pcoa.export, coldata_order,by.x="row.names",by.y = "sample_name")
row.names(braycurtis.pcoa.export)<-braycurtis.pcoa.export[,1]
braycurtis.pcoa.export<-braycurtis.pcoa.export[,-1]
## plot PCoA
b<-braycurtis.pcoa[["values"]][["Relative_eig"]]
#barplot(b[b>0],names.arg =colnames(braycurtis.pcoa.export))
library(forcats)
if (names(braycurtis.pcoa.export)[2]=="Axis.2"){
# reorder the group column following the value of order column
braycurtis.pcoa.export %>%
mutate(group = fct_reorder(group, order)) %>%
ggplot(aes(Axis.1, Axis.2,color=group)) +
geom_point(size=3) +
xlab(paste0("PCoA1: ",round(100*b[1]),"% variance")) +
ylab(paste0("PCoA2: ",round(100*b[2]),"% variance")) +
geom_text_repel(aes(label=row.names(braycurtis.pcoa.export)), size=3, max.overlaps = Inf, segment.size = 0.5) +
coord_fixed() +
theme_bw() +
ggtitle("Bray-Curtis Distances PCoA")
ggsave("PCoA_label_color.pdf")
braycurtis.pcoa.export %>%
mutate(group = fct_reorder(group, order)) %>%
ggplot(aes(Axis.1, Axis.2,color=group)) +
geom_point(size=3) +
xlab(paste0("PCoA1: ",round(100*b[1]),"% variance")) +
ylab(paste0("PCoA2: ",round(100*b[2]),"% variance")) +
coord_fixed() +
theme_bw() +
ggtitle("Bray-Curtis Distances PCoA")
ggsave("PCoA_color.pdf")
} else {
write("No Axis.2 was found on PCoA","No_Axis2_on_PCoA.txt")
}
# anosim test
data2<-t(as.matrix(data1@otu_table))
pathotype.anosim <- anosim(data2, braycurtis.pcoa.export$group)
# plot results
pdf("ANOSIM.pdf")
plot(pathotype.anosim,
main="Analysis of Diversity in Groups",
xlab="",
ylab="")
dev.off()
# Start writing to an output file
sink('ANOSIM-analysis-output.txt')
summary(pathotype.anosim)
# Stop writing to the file
sink()
# plot heatmap (Phyloseq style)
sampledata<-as.data.frame(coldata[,2])
row.names(sampledata)<-coldata[,1]
colnames(sampledata)<-"Groups"
sam = sample_data(sampledata)
sp = otu_table(norm, taxa_are_rows = TRUE)
physeq<-phyloseq(sp, sam)
if (nrow(norm)>100){
ph<-plot_heatmap(physeq,max.label = nrow(norm)) +
theme (axis.text.y = element_text(size=(2.2*225/nrow(norm))))
} else {
ph<-plot_heatmap(physeq)
}
ph$labels$y<-"Species"
print(ph)
ggsave("Heatmap_all.png")
ggsave("Heatmap_all.pdf")
# Add sample data
tax = tax_table(data.adj)
otu = otu_table(data.adj)
data_sam <- phyloseq(otu,tax, sam)
# change color scale for plot_bar
HowManyPhyla <- length(levels(as.factor(data_sam@tax_table[,2])))
getPalette = colorRampPalette(brewer.pal(12, "Set2"))
PhylaPalette = getPalette(HowManyPhyla)
# phyloseq bar plots
try(plot_bar(data, fill = "Phylum") +
scale_fill_manual(values = PhylaPalette))
try(ggsave("Bar_phy.pdf", limitsize = FALSE))
try(plot_bar(data_sam, fill = "Phylum", facet_grid = ~Groups) +
scale_fill_manual(values = PhylaPalette))
try(ggsave("Bar_group_phy.pdf", limitsize = FALSE))
# relative abundance bar plot
data_sam_relabund<-transform_sample_counts(data_sam, function(x) x / sum(x))
theme_set(theme_grey())
plot_bar(data_sam_relabund, fill="Phylum") +
geom_bar(stat="identity", position="stack") +
labs(x = "", y = "Relative Abundance\n") +
theme(panel.background = element_blank()) +
scale_fill_manual(values =PhylaPalette)
ggsave("Bar_relative_phy.pdf")
# alpha diversity box plot
theme_set(theme_bw())
plot_richness(data_sam,"Groups",measures = c("Shannon","Simpson")) +
geom_boxplot()
ggsave("Alpha_diversity.pdf")
theme_set(theme_grey())
# alpha diversity comparisons
data.alpha_g<-merge(data.alpha,coldata,by.x="row.names",by.y=1)
library(ggpubr)
my_comparisons<-list()
for (i in 1:nrow(coldata_vs)){
my_comparisons[[i]] <- c(coldata_vs$group1[i],coldata_vs$group2[i])
}
ggboxplot(data.alpha_g, x = "group", y = "Shannon",
color = "group", palette = "jco",
add = "jitter") +
stat_compare_means(comparisons = my_comparisons,
method = "t.test") # Add pairwise comparisons p-value
ggsave("Alpha_diversity_Shannon.pdf")
ggboxplot(data.alpha_g, x = "group", y = "Simpson",
color = "group", palette = "jco",
add = "jitter") +
stat_compare_means(comparisons = my_comparisons,
method = "t.test")
ggsave("Alpha_diversity_Simpson.pdf")
}
## draw volcano and bar plot
for (i in 1:nrow(coldata_vs)){
group1<-coldata_vs$group1[i]
group2<-coldata_vs$group2[i]
pvalue_name<-paste0("padj.",group1,"_vs_",group2) # get the adjusted p-value
log2FoldChange_name<-paste0("log2FoldChange.",group1,"_vs_",group2)
# nt4v<-cbind(DEG[,"gene_name"],DEG[,pvalue_name],
# DEG[,log2FoldChange_name])
if (filename == "host_counts.txt"){
nt4v<-cbind(DEG["gene_name"],DEG[pvalue_name],
DEG[log2FoldChange_name])
colnames(nt4v)<-c("gene_name","pvalue","log2FoldChange")
# nt4v<-as.data.frame(nt4v) # volcano plot require data.frame
# nt4v$pvalue<-as.numeric(nt4v$pvalue) #character to numeric
# nt4v$log2FoldChange<-as.numeric(nt4v$log2FoldChange)
# pass undefined gene name
while ("-" %in% nt4v$gene_name){
nt4v$gene_name[match("-",nt4v$gene_name)] <-
DEG$gene_id[match("-",DEG$gene_name)]
}
# drop duplicated row
nt4v<-nt4v[!duplicated(nt4v$gene_name),]
# The significantly differentially expressed genes are the ones found in the upper-left and upper-right corners.
# Add a column to the data frame to specify if they are UP- or DOWN- regulated (log2FoldChange respectively positive or negative)
# add a column of NAs
nt4v$diffexpressed <- "NO"
# if log2Foldchange > 0.5 and pvalue < 0.05, set as "UP"
nt4v$diffexpressed[nt4v$log2FoldChange > 0.5 & nt4v$pvalue < 0.05] <- "UP"
# if log2Foldchange < -0.5 and pvalue < 0.05, set as "DOWN"
nt4v$diffexpressed[nt4v$log2FoldChange< -0.5 & nt4v$pvalue < 0.05] <- "DOWN"
## Volcano plotting
# v<-ggplot(data=nt4v,
# aes(x=log2FoldChange,y=-log10(pvalue)),
# col=diffexpressed) +
# geom_point() +
# theme_minimal()
# # Add vertical lines for log2FoldChange thresholds, and one horizontal line for the p-value threshold
# v2<-v+geom_vline(xintercept = c(-0.5,0.5), col="red") +
# geom_hline(yintercept=-log10(0.05), col="red")
# # Change point color
# mycolors <- c("blue", "red", "black")
# names(mycolors) <- c("DOWN", "UP", "NO")
# v3 <- v2 + scale_colour_manual(values = mycolors)
diffexpressed<-nt4v[nt4v$diffexpressed != "NO",]
# select top 20 DEG of FC
topFC<-rbind(top_n(diffexpressed,20,log2FoldChange),top_n(diffexpressed,-20,log2FoldChange))
topFC$label<-topFC$gene_name
topFC<-topFC[!duplicated(topFC$gene_name),]
# Create a new column "label" to nt4v, that will contain the name of genes differentially expressed (NA in case they are not)
nt4v_label<-merge(nt4v,topFC, all=T)
} else {
nt4v<-cbind(DEG["Name"],DEG[pvalue_name],
DEG[log2FoldChange_name])
colnames(nt4v)<-c("Name","pvalue","log2FoldChange")
# pass undefined gene name
while ("-" %in% nt4v$Name){
nt4v$Name[match("-",nt4v$Name)] <-
DEG$gene_id[match("-",DEG$Name)]
}
# drop duplicated row
nt4v<-nt4v[!duplicated(nt4v$Name),]
# The significantly differentially expressed genes are the ones found in the upper-left and upper-right corners.
# Add a column to the data frame to specify if they are UP- or DOWN- regulated (log2FoldChange respectively positive or negative)
# add a column of NAs
nt4v$diffexpressed <- "NO"
# if log2Foldchange > 0.5 and pvalue < 0.05, set as "UP"
nt4v$diffexpressed[nt4v$log2FoldChange > 0.5 & nt4v$pvalue < 0.05] <- "UP"
# if log2Foldchange < -0.5 and pvalue < 0.05, set as "DOWN"
nt4v$diffexpressed[nt4v$log2FoldChange< -0.5 & nt4v$pvalue < 0.05] <- "DOWN"
diffexpressed<-nt4v[nt4v$diffexpressed != "NO",]
# select top 20 DEG of FC
topFC<-rbind(top_n(diffexpressed,20,log2FoldChange),top_n(diffexpressed,-20,log2FoldChange))
topFC$label<-topFC$Name
topFC<-topFC[!duplicated(topFC$Name),]
# Create a new column "label" to nt4v, that will contain the name of genes differentially expressed (NA in case they are not)
nt4v_label<-merge(nt4v,topFC, all=T)
}
## Volcano plotting
# plot adding up all layers we have seen so far
ggplot(data=nt4v_label, aes(x=log2FoldChange, y=-log10(pvalue),
col=diffexpressed, label=label)) +
geom_point(size = 1.5) +
theme_minimal() +
geom_text_repel(size=3, max.overlaps = Inf) +
scale_color_manual(values=c("blue", "black", "red")) +
geom_vline(xintercept=c(-0.5, 0.5), col="red", linetype="dashed", linewidth = 0.5) +
geom_hline(yintercept=-log10(0.05), col="red", linetype="dashed", linewidth = 0.5) +
ggtitle(paste0(group1,"_vs_",group2)) +
theme(plot.title = element_text(hjust = 0.5))
ggsave(paste0(group1,"_vs_",group2,"/Volcano_",group1,"_vs_",group2,".pdf"))
## bar plotting
bar <- diffexpressed
if (nrow(bar)!=0){
# sort log2FoldChange top to down
if (filename == "host_counts.txt"){
bar$c <- with(bar,reorder(gene_name,log2FoldChange))
} else {
bar$c <- with(bar,reorder(Name,log2FoldChange))
}
bar_plot<-ggplot(bar,aes(x=log2FoldChange,y=c,fill=pvalue))+
geom_bar(stat="identity",aes(fill=diffexpressed), width=0.5) +
#scale_fill_brewer(palette="Blues")+
scale_fill_manual(name="Expression",
labels = c("Down", "Up"),
values = c("DOWN"="#00ba38", "UP"="#f8766d")) +
#scale_fill_gradient2(low=rgb(14,37,56,max=255),high=rgb(54,169,243,max=255), mid=rgb(52,109,157,max=255), midpoint=0.01)+
#scale_fill_gradient(low=rgb(54,169,243,max=255), high=rgb(14,37,56,max=255))+
#scale_fill_continuous_sequential(palette = "Blues3", begin=0.4)+
labs(x="log2FoldChange",y=" ") + #could add ,fill="n=", title = "BMC_HvsBMC_L_DOWN")
ggtitle(paste0(group1,"_vs_",group2)) +
#geom_text(aes(x=log2FoldChange+0.6),label="*") +
theme_bw() +
scale_y_discrete(breaks=bar[,1],labels=str_trunc(bar[,1],40)) +
guides(fill = guide_legend(reverse = TRUE)) #reverse the legend to put "Up" in an upper position
ggsave(paste0(group1,"_vs_",group2,"/Barplot_",group1,"_vs_",group2,".pdf"),height=0.2*length(bar_plot[["data"]][[1]]),limitsize = FALSE)
}
}
## Venn Diagram
if (length(unique(coldata$group))>=2){
dv.list<-list()
for (i in unique(coldata$group)){
vd<-coldata[coldata$group==i,]
sample_name<-as.character(vd[,1])
sample_name.norm<-paste0(sample_name,".norm")
if (filename == "host_counts.txt"){
vd.df<-DEG[c("gene_name",sample_name.norm)]
} else {
vd.df<-DEG[c("Name",sample_name.norm)]
}
#Removing rows having all zeros, ignore 1st column with names
vd.df<-vd.df[rowSums(vd.df[-1])>0,]
#add to list
dv.list[[i]]<-vd.df[,1]
}
#myCol <- brewer.pal(length(unique(coldata$group)), "Pastel2")
library(VennDiagram)
# Don't write log file for VennDiagram
futile.logger::flog.threshold(futile.logger::ERROR, name = "VennDiagramLogger")
#venn.diagram(...)
venn.diagram(
x = dv.list,
category.names = names(dv.list),
filename = 'venn_diagramm.png',
output=TRUE,
# # Output features
imagetype="png" ,
# height = 480 ,
# width = 480 ,
# resolution = 300,
compression = "lzw",
# Circles
size = 1,
#lty = 'blank',
col=cols,
#fill = myCol,
# keep same colors with heatmap and PCA
fill = alpha(cols,0.6),
# Numbers
#cex = 1,
fontface = "bold",
fontfamily = "sans",
# Set names
#cat.cex = 1,
cat.fontface = "bold",
# cat.default.pos = "outer",
# cat.pos = c(-27, 27, 135),
# cat.dist = c(0.055, 0.055, 0.085),
cat.fontfamily = "sans",
#rotation = 1
)
}
# non_host_host_reads_ratio comparison
if (filename %in% c("bracken_species_all","bracken_phylum_all","bracken_genus_all")){
files_h <- list.files(paste0(dirname(args[1]),"/temp"), pattern="^Report_host_.*\\.txt$", full.names=TRUE, recursive=FALSE)
lh<-c() #generate a empty list
la<-c()
for (i in files_h){
t<-read.table(i,sep="\t", quote= "")
a<-t[1,1] #get unclassified reads ratio: non-host part
r<-a/(100-a) #non_host_host_reads_ratio in kraken2 host report
fn<-gsub("^Report_host_|\\.txt$","",basename(i)) #grep the sample name
lh<-c(lh,setNames(r,fn)) #add sample name with value to the list lh; non_host_host_reads_ratio
la<-c(la,setNames(a,fn)) #unclassified reads ratio: non-host part
}
lah<-cbind(la,lh)
lah<-merge(lah,coldata,by.x="row.names",by.y="sample_name")
colnames(lah)<-c("Sample","unclassified_reads_ratio_percent","non_host_host_reads_ratio","Groups")
# to draw box plot with comparison of unclassified ratio