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metabonome_pipline.R.backup_20200601
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#!/usr/bin/env Rscript
library(optparse)
library(purrr)
#######arguments
option_list <- list(
make_option(c("-i", "--input"),metavar="path", dest="cpd",help="Specify the path of metabolites abundance table. Required",default=NULL),
make_option(c("-m", "--map"),metavar="path",dest="map", help="Specify the path of mapping file. Required",default=NULL),
# make_option(c("-q", "--qc-meta"),metavar="path",dest="qc", help="Specify the path of metadata file for QC.",default=NULL),
make_option(c("-p", "--pca"),metavar="paths",dest="pca", help="comma seprated paths to QC completed compound tables.",default=NULL),
make_option(c("-c", "--category"),metavar="string",dest="category", help="Category to compare. Required",default=NULL),
make_option(c("-d", "--database"),metavar="path", dest="db",help="Path to the database directory.",default="/home/cheng/Databases/Metabonome_database/database"),
make_option(c("-s", "--species"),metavar="string", dest="species",help="The organism type of metabolites. Available now: hsa, mmu, rno",default="hsa"),
make_option(c("-t", "--type"),metavar="string", dest="type",help="The metabonome type, (target, untarget or untarget_gc)",default="untarget"),
make_option(c("-n", "--normalize"),metavar="string", dest="normalize",help="Wheather to normalize the data, (yes, no)",default="yes"),
make_option(c("-o", "--output"),metavar="directory",dest="out", help="Specify the directory of output files",default="./")
)
opt <- parse_args(OptionParser(option_list=option_list,description = "The metabonome analysis pipeline"))
#####packages
library("purrr")
library("RColorBrewer")
library("statTarget")
library("getopt")
library("stringr")
library("VennDiagram")
# library("ggbiplot")
#####arguments
query_type = "kegg_id"
out_dir = opt$out # "/home/cheng/pipelines/testdir/cpd_pipline"
db_dir = opt$db # "/home/cheng/pipelines/Metabonome/database/"
cpd_file = opt$cpd # "/home/cheng/pipelines/testdir/cpd_pipline/compound_abundance.csv"
# metadata_file = opt$qc # "/home/cheng/pipelines/testdir/cpd_pipline/metadata.csv"
# run_qc <- !is.null(metadata_file)
map_file = opt$map # "/home/cheng/pipelines/testdir/cpd_pipline/mapping_file.txt"
categories = str_split(opt$category, ',')[[1]]
organism = opt$species
base_dir<-normalizePath(dirname(get_Rscript_filename()))
source(paste(base_dir, "metabonome_core.R", sep = "/"))
pcas <- opt$pca
input_type <- opt$type
NORMALIZE_DATA <<- opt$normalize=="yes"
#TEST
# setwd("/media/cheng/disk1/Metabonome_Projects/cheli")
# query_type = "kegg_id"
# out_dir = "results" # "/home/cheng/pipelines/testdir/cpd_pipline"
# db_dir = "/home/cheng/Databases/Metabonome_database/database"
# cpd_file = "msms.csv" # "/home/cheng/pipelines/testdir/cpd_pipline/compound_abundance.csv"
# metadata_file = NULL # "/home/cheng/pipelines/testdir/cpd_pipline/metadata.csv"
# run_qc <- !is.null(metadata_file)
# map_file = "map.csv" # "/home/cheng/pipelines/testdir/cpd_pipline/mapping_file.txt"
# categories = "Category"
# organism = "rno"
# base_dir<-"/home/cheng/pipelines/Metabonome/"
# source(paste(base_dir, "metabonome_core.R", sep = "/"))
# pcas <- "data/metabolome_neg.csv,data/metabolome_pos.csv"
# input_type <- "untarget"
#####arguments check
if(!dir.exists(out_dir)){
dir.create(out_dir)
}
out_dir <- normalizePath(out_dir)
db_dir <- normalizePath(db_dir)
cpd_file <- normalizePath(cpd_file)
map_file <- normalizePath(map_file)
root_dir <- paste(out_dir, "/ResultsMetabonome", sep = "")
pca_files <- choose(is.null(pcas), pcas, normalizePath(str_split(pcas, ',')[[1]]))
sprintf("Results is saved at %s", root_dir)
######dir preparation
moveto_dir <- function(dir="/"){
dir <- paste(root_dir, dir, sep = "/")
if(!dir.exists(dir)){
dir.create(dir, recursive = TRUE)
}
setwd(dir = dir)
}
set_db_location(db_dir)
write("#####################Read Table", stdout())
# cpd_table <- read.table(cpd_file, sep = "\t", header = T, row.names = 1, stringsAsFactors = FALSE)
mapping_table <- read.csv(map_file, header = T, row.names = 1, stringsAsFactors = FALSE)
sample_id_map <- c()
actual_samid <- mapping_table[, ncol(mapping_table)]
sample_id_map[rownames(mapping_table)] <- actual_samid
rownames(mapping_table) <- actual_samid
write("#####################Color Assignment", stdout())
pallet <- unique(c(c("#20B2AA", "#DC143C", "#FF9933"), rev(brewer.pal(12,"Paired")),brewer.pal(8,"Set2")[-c(7,8)],brewer.pal(8,"Dark2"),brewer.pal(12,"Set3"),brewer.pal(8,"Accent"),brewer.pal(11,"Spectral")))
group_df <- na.omit(melt(mapping_table[categories]))
all_groups <- unique(group_df[,ncol(group_df)])
all_groups <- sort(all_groups)
group_colors <- pallet[1:length(all_groups)]
names(group_colors) <- all_groups
ordered_samples <- rownames(mapping_table)[order(mapping_table["order"][, 1])]
yield_combine <- function(vec){
vec <- sort(vec)
out_list <- list()
index <- 1
for(i in 2:length(vec)){
sub_com <- combn(vec, i)
for(j in 1:ncol(sub_com)){
out_list[[index]] <- sub_com[, j]
index = index +1
}
}
return(out_list)
}
select_num<-function(df, rownamecol=NA, save_col=NA){
if(!is.na(rownamecol)){
rownames(df) <- df[rownamecol][, 1]
}
col_is_num <- c()
for(c in 1:ncol(df)){
col_is_num[c] <- is.numeric(df[, c])
}
df_num <- df[, col_is_num]
if(!is.na(save_col)){
df_num <- data.frame(df[save_col], df_num, check.rows = TRUE, check.names = FALSE)
}
return(df_num)
}
select_cpd_df <- function(cpd_label="name", fix_columns = c(1:9)){
label <- cpd_df[cpd_label][,1]
selected <- !(is.na(label)|label=="")
out_df <- cpd_df[selected, ]
label <- label[selected]
out_df <- out_df[,-fix_columns]
out_df <- apply(out_df, 2, function(x){return(base::tapply(x, INDEX = label, sum))})
return(t(out_df))
}
data_factory <- function(category="Category", cpd_label = "name", type = "pair", cmpType=1, fix_columns = c(1:9)){
map_df <- mapping_table[category]
map_df <- na.omit(map_df)
#print(map_df)
#print(select_cpd_df(cpd_label = cpd_label, fix_columns = fix_columns))
sub_cpd_df <- select_cpd_df(cpd_label = cpd_label, fix_columns = fix_columns)
map_samples <- rownames(map_df)
cpd_samples <- rownames(sub_cpd_df)
intersect_samples <- intersect(cpd_samples, map_samples)
print(sprintf("FOR %s:", category))
print("Following samples found in mapping file, but not in compound abundance table:")
print(map_samples[!map_samples%in%cpd_samples])
print("Following samples found in abundance table, but not in compound mapping file:")
print(cpd_samples[!cpd_samples%in%map_samples])
map_df <- map_df[map_samples%in%intersect_samples, ]
map_df <- data.frame(map_df, check.names = FALSE, stringsAsFactors = FALSE)
colnames(map_df) <- category
rownames(map_df) <- map_samples[map_samples%in%intersect_samples]
sub_cpd_df <- sub_cpd_df[cpd_samples%in%intersect_samples, ]
# print(map_df)
# print(sub_cpd_df)
base_df <- rownames_join(map_df, sub_cpd_df)
# print(base_df)
base_df <- base_df[order(base_df[,1]), ]
base_df <- data.frame(SampleID=rownames(base_df), base_df, check.names = FALSE, stringsAsFactors = FALSE)
out_list <- list()
if(type=="pair"){
groups <- unique(map_df[,1])
paired_groups <- yield_combine(groups)
nums <- length(paired_groups)
cmpType <- choose(length(cmpType)==1, rep(cmpType, nums), cmpType)
for(i in 1:nums){
ele_groups <- paired_groups[[i]]
ele_name <- reduce(ele_groups, paste, sep=".vs.")
ele_is_paired <- length(ele_groups)==2
ele_cmpType <- cmpType[i]
ele_data <- base_df[base_df[,2]%in%ele_groups, ]
# 除去所有值都是NA的化合物
ele_data <- ele_data[, c(T,T,colSums(!is.na(ele_data[,-c(1,2)]))>0)]
ele_colors <- group_colors[ele_groups]
out_list[[i]] <- list(data=ele_data, name=ele_name, groups=ele_groups, colors=ele_colors, cmpType=ele_cmpType, paired=ele_is_paired)
}
return(out_list)
}else{
if(type=="single"){
# print("Here !!!!!!!!!!!!!!!!!!!!!!!!!!!")
groups <- unique(base_df[, 2])
for (i in 1:length(groups)) {
ele_data = base_df[base_df[, 2]==groups[i], ]
ele_data <- ele_data[, c(T,T,colSums(!is.na(ele_data[,-c(1,2)]))>0)]
out_list[[i]] <- list(name=groups[i], data=ele_data, colors=group_colors[groups[i]])
}
}else{
out_list <- base_df
}
}
return(out_list)
}
map_table_header <- function(cpd_table){
sample_index <- colnames(cpd_table)%in%names(sample_id_map)
colnames(cpd_table)[sample_index] <- sample_id_map[colnames(cpd_table)[sample_index]]
return(cpd_table)
}
for(category in categories){
# category <- "Category"
write("#####################Data Prepare", stdout())
moveto_dir(paste(category, "/00-DataProcess/", sep = "/"))
# map
cpd_table <- read.csv(cpd_file, check.names = FALSE, header = T, stringsAsFactors = FALSE)
cpd_table <- map_table_header(cpd_table=cpd_table)
if(input_type=="untarget"){
# if(run_qc){
# dir.create("./01-QA_QC")
# metadata <- read.csv(metadata_file, row.names = 1, check.names = FALSE, stringsAsFactors = FALSE)
# map_category <- mapping_table[category]
# metadata <- rownames_join(metadata, map_category)
# colnames(metadata)[3] <- "class"
# write.csv(data.frame(sample=rownames(metadata), metadata, check.names = FALSE), file = "metadata.csv", row.names = FALSE)
# shiftCor("metadata.csv", cpd_file, Frule = 0.8, MLmethod = "QCRFSC", QCspan = 0,imputeM = "KNN", plot = TRUE)
# cpd_table <- read.csv("statTarget/shiftCor/After_shiftCor/shift_sample_cor.csv", check.names = FALSE, header = T, row.names = 1, stringsAsFactors = FALSE)
# system("mv statTarget/shiftCor/* ./01-QA_QC")
# # system("mv statTarget/shiftCor/RSDresult/* 01-QA_QC")
# system("rm -r statTarget")
# cpd_table <- t(cpd_table[, -1])
# cpd_names <- rownames(cpd_table)
# ref_df <- cross_reference(cpd_names, type="name")
# # rownames(ref_df)<-ref_df[,1]
# cpd_df <- data.frame(name=cpd_names, ref_df, cpd_table, check.names=FALSE)
# }else{
cpds <- cpd_table[, 1] # First column is keggid
ref_df <- cross_reference(cpds, type="kegg_id")
# Second column is metabolite name
cpd_df <- data.frame(cpd_table[, 2], ref_df, cpd_table[, -c(1, 2)], check.names=FALSE)
colnames(cpd_df)[1] <- "name"
# cpd_df["kegg_id"] <- cpds
# }
cpd_df<-cpd_df[, -c(2,3)]
write.csv(cpd_df, "compound_cross_reference_after_QC.csv", row.names = F)
fix_columns <- c(1:9)
}else{
if(input_type=="target"){
# cpd_df <- read.csv(cpd_file, check.names = FALSE, header = T, stringsAsFactors = FALSE)
cpd_df <- cpd_table
# colnames(cpd_df)[1:2]<-c("name", "class")
# fix_columns <- c(1:2)
colnames(cpd_df)[1]<-c("name")
fix_columns <- c(1)
}else{
cpd_names <- cpd_table[, 1]
ref_df <- cross_reference(cpd_names, type="name")
cpd_df <- data.frame(cpd_names, ref_df, cpd_table[, -c(1)], check.names=FALSE)
colnames(cpd_df)[1] <- "name"
cpd_df<-cpd_df[, -c(2,3)]
write.csv(cpd_df, "compound_cross_reference_after_QC.csv", row.names = F)
fix_columns <- c(1:9)
}
}
rownames(cpd_df) <- cpd_df[, 1]
write("#####################QC plot", stdout())
moveto_dir(paste(category, "/00-DataProcess/01-QA_QC", sep = "/"))
if(length(pca_files)>0){
for(pca_file in pca_files){
print(sprintf("Plot PCA for %s", pca_file))
pca_df <- read.csv(pca_file, header=TRUE, row.names=1, check.names=FALSE, stringsAsFactors=FALSE)
pca_df <- map_table_header(pca_df)
pca_df <- t(pca_df)
sam_gp <- rownames(pca_df)
is_qc <- grepl("QC", sam_gp)
sam_gp[is_qc] <- "QC"
sam_gp[!is_qc] <- "Sample"
pca.data <- data.frame(Sample=rownames(pca_df), Category=sam_gp, pca_df)
rownames(pca.data)<-rownames(pca_df)
# pdf("")
file=paste(str_replace(pca_file, "\\.[^\\.]+$", ""), "_PCA_QC.pdf", sep="")
file_withid=paste(str_replace(pca_file, "\\.[^\\.]+$", ""), "_PCA_QC_with_ids.pdf", sep="")
file=str_replace(file, "^.+/", "")
file_withid=str_replace(file_withid, "^.+/", "")
# print(pca.data)
elipse_pca(table = pca.data, file_biplot = file, file_pc1 = NA, label = F)
elipse_pca(table = pca.data, file_biplot = file_withid, file_pc1 = NA, label = T)
}
}
write("#####################QA plot", stdout())
data <- data_factory(category = category, type = "all", fix_columns=fix_columns)
# print(data)
elipse_pca(table = data, file_pc1 = "AllSample_PC1.pdf", file_biplot = NA, sam_order = ordered_samples)
datas <- data_factory(category = category, type = "single", fix_columns=fix_columns)
for (data in datas) {
elipse_pca(table = data$data, colors = data$colors, file_biplot = paste(data$name, "_PCA_QA.pdf", sep = ""), file_pc1 = NA, label = T)
elipse_pca(table = data$data, colors = data$colors, file_biplot = paste(data$name, "_PCA_QA_without_ids.pdf", sep = ""), file_pc1 = NA, label = F)
}
write("#####################Data Normlization", stdout())
moveto_dir(paste(category, "/00-DataProcess/02-Normlization", sep = "/"))
df <- data_factory(category=category, type = "all", fix_columns=fix_columns)
mSet <- prepare(df)
mSet <- PlotNormSummary(mSetObj = mSet, "compound_wise_normlization.pdf", format = "pdf", dpi = 300, width = 30)
mSet <- PlotSampleNormSummary(mSetObj = mSet, "sample_wise_normlization.pdf", format = "pdf", dpi = 300, width = 30)
write.csv(mSet$dataSet$row.norm, "sample_wise_normalized.csv")
write.csv(mSet$dataSet$norm, "compound_wise_normalized.csv")
if("class" %in% colnames(cpd_df)){
datas <- data_factory(category = category, cpd_label = "class", fix_columns=fix_columns)
for(data in datas){
write("#####################ConcentrationSummary", stdout())
moveto_dir(paste(category, "01-ConcentrationSummary/1-Barplot", data$name, sep = "/"))
abundance_barplot(data$data, colors = data$colors, by_group_mean = FALSE, prefix = "Compounds_with_Biological_Roles_")
abundance_barplot(data$data, colors = data$colors, by_group_mean = TRUE, prefix = "Compounds_with_Biological_Roles_group_mean_")
}
}
datas <- data_factory(category = category, fix_columns=fix_columns)
report_group <- datas[[1]]$name
sig_features_set <- list()
for(data in datas){
write("#####################ConcentrationSummary", stdout())
moveto_dir(paste(category, "01-ConcentrationSummary/1-Barplot", data$name, sep = "/"))
abundance_barplot(data$data, colors = data$colors, by_group_mean = FALSE)
abundance_barplot(data$data, colors = data$colors, by_group_mean = TRUE, prefix = "group_mean_")
moveto_dir(paste(category, "01-ConcentrationSummary/2-Heatmap", data$name, sep = "/"))
abundance_heatmap(data$data, colors = data$colors, by_group_mean = FALSE, cluster = FALSE)
abundance_heatmap(data$data, colors = data$colors, by_group_mean = FALSE, cluster = TRUE, prefix = "clustered_")
abundance_heatmap(data$data, colors = data$colors, by_group_mean = TRUE, cluster = FALSE, prefix = "group_mean_")
write("#####################PCA Analysis", stdout())
moveto_dir(paste(category, "02-PCA", sep = "/"))
PCA(data$data, out_dir = data$name, colors = data$colors)
write("#####################PLSDA Analysis", stdout())
moveto_dir(paste(category, "03-PLSDA", sep = "/"))
PLSDA(data$data, out_dir = data$name, colors = data$colors)
write("#####################OPLSDA Analysis", stdout())
moveto_dir(paste(category, "04-OPLSDA", sep = "/"))
OPLSDA(data$data, out_dir = data$name, colors = data$colors)
write("#####################UnivariateAnalysis Analysis", stdout())
moveto_dir(paste(category, "05-UnivariateAnalysis", data$name, sep = "/"))
if(data$paired){
print("Paired data, plot valcano")
Volcano(data$data, colors = data$colors, cmpType = data$cmpType)
sig_features <- sig_boxplot(table = data$data, colors = data$colors)
# print(sig_features)
if(length(sig_features)>0){
sig_features_set[[data$name]] <- sig_features
}
}else{
sig_features <- sig_boxplot(table = data$data, colors = data$colors)
}
write("#####################RandomForest Analysis", stdout())
moveto_dir(paste(category, "06-RandomForest", sep = "/"))
RF(data$data, out_dir = data$name)
write("#####################SupportVectorMachine Analysis ", stdout())
moveto_dir(paste(category, "07-SupportVectorMachine", sep = "/"))
if(data$paired){
SVM(data$data, out_dir = data$name)
}
}
write("#####################Venn Plot ", stdout())
# if(length(sig_features_set)>1){
# moveto_dir(paste(category, "05-UnivariateAnalysis", sep = "/"))
# vs_combs <- yield_combine(names(sig_features_set))
# for (comb in vs_combs) {
# if(length(comb)<6){
# file_base <- reduce(comb, paste, sep="_and_")
# venn.diagram(sig_features_set[comb],
# filename =paste(file_base, "_venn.tiff", sep = ""),
# imagetype="tiff",alpha= 0.50,lwd =1.2,cat.cex=1.4,
# fill=rainbow(length(comb)),
# margin=0.15)
# }
# }
# system("rm *.log")
# }
if(!input_type=="target"){
datas <- data_factory(category = category, cpd_label = "kegg_id", fix_columns=fix_columns)
for(data in datas){
write("#####################PathwayTopoEnrichment Analysis ", stdout())
moveto_dir(paste(category, "09-PathwayTopoEnrichment", data$name, sep = "/"))
pathway_analysis(data$data, out_dir = "./", threshp = 0.05, organism = organism)
tryCatch({
if(file.exists("selected_compounds.csv")){
df <- read.csv("selected_compounds.csv", row.names = 1, check.names = FALSE, stringsAsFactors = FALSE)
ora.df <- read.csv("pathway_enrichment_and_topo_results.csv", row.names = 1, check.names = FALSE, stringsAsFactors = FALSE)
rownames(ora.df) <- str_replace(rownames(ora.df), '^\\D*', 'map')
write.csv(ora.df, "pathway_enrichment_and_topo_results.csv")
gc <- groupCenter(prepare(data$data))
df <- rownames_join(df, gc)
write.csv(df, "selected_compounds.csv")
sig_pathways <- ora.df["Description"][ora.df["Raw p"]<=0.05]
sig_pathways_id <- rownames(ora.df)[ora.df["Raw p"]<=0.05]
ele_colors <- data$colors
ele_colors <- paste(reduce(names(ele_colors), paste, sep=","), reduce(ele_colors, paste, sep=","), sep = ";")
for(i in 1:length(sig_pathways_id)){
mapid <- paste("map", str_extract(sig_pathways_id[i], "\\d+"), sep = "")
if(!(mapid=="map"|mapid=="mapNA")){
print(mapid)
sig_path <- str_replace_all(sig_pathways[i], " |,", "_")
cmd <- sprintf("CColorMap.py -i selected_compounds.csv -m %s -c '%s' -n 'Which-Max' -p '%s'", mapid, ele_colors, sig_path)
print(cmd)
system(cmd)
}
}
}
}, error=function(e){print(e)})
}
}
show_label <- !input_type=="untarget"
datas <- data_factory(category = category, type = "single", fix_columns=fix_columns)
for (data in datas) {
# data <- datas[[4]]
write("#####################Correlation Analysis", stdout())
moveto_dir(paste(category, "08-CorrelationAnalysis", data$name, sep = "/"))
group_cor_heatmap(data$data[, -c(1,2)], show_names = show_label)
# group_cor_heatmap(data$data[, -c(1,2)], cluster = TRUE, prefix = "clustered_", show_names = show_label)
}
data <- data_factory(category = category, type = "all", fix_columns=fix_columns)
data <- data[, -c(1, 2)]
moveto_dir(paste(category, "08-CorrelationAnalysis", sep = "/"))
group_cor_heatmap(data, prefix = "all_group_", show_names = show_label)
# group_cor_heatmap(data, cluster = TRUE, prefix = "all_group_clustered_", show_names = show_label)
moveto_dir(category)
system(sprintf("cp -rp %s/FiguresTablesForReport ./", base_dir))
system("mv FiguresTablesForReport/缁撻鎶ュ憡.html ./")
# system(sprintf("cp 09-PathwayTopoEnrichment/%s/%s_dpi300.pdf FiguresTablesForReport/鍥?9-3.pdf", report_group, str_replace_all(REPORT_PATHWAY, " +", "_")))
system(sprintf("bash %s/prepare_report_images.sh %s", base_dir, report_group))
system(sprintf("render_report.py 缁撻鎶ュ憡.html %s", input_type))
}