/
training.R
executable file
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training.R
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args <- commandArgs(trailingOnly=TRUE)
##for test
#args <- c("12304", "12304", "auc_recomputed", "1", "1", "TRUE", "ccle_gdsc", "glm", "gaussian", "all", "../results/training_results", "TRUE", "expression.cut.off")
##
if(!require("np")){install.packages("np");library(np)}
if(!require("Hmisc")){install.packages("Hmisc");library(Hmisc)}
options(echo=TRUE) # if you want see commands in output file
print(args)
source("foo_training.R")
sensitivity.method <- as.character(args[3])# c("auc_published", "auc_recomputed", "slope_recomputed", "ic50_published", "ic50_recomputed")
RNA_seq.normalize <- as.character(args[6])# c(TRUE,FALSE)
training.method <- as.character(args[7]) # c("ccle_gdsc", "gray", "ccle", "gdsc", "gCSI")
model.method <- as.character(args[8])# c("glm", "npreg", "penalized")
glm.family <- as.character(args[9])# c("binomial", "gaussian")
tissue <- NULL
if(as.character(args[10]) != "all") {tissue <- as.character(args[10])} #c(NULL, tissue.type)
output.folder <- as.character(args[11]) #output folder
subset.genes <- as.character(args[12])# c(TRUE,FALSE)
subset.genes.method <- as.character(args[13]) #c("expression.cut.off", "biotype")
training.input <- as.character(args[14])
statistical.method <- "bootstrap" #c("anova", "crossvalidation", "bootstrap")
effect.size <- "r.squared & cindex" #c("adj.r.squared", "r.squared", "rmsd", "cindex","r.squared & cindex")
myf <- file.path(path.data, "training_ccle_gdsc.RData")
load("../data/ensembl.map.genes.isoforms.GRCh38.87.RData", verbose=TRUE)
source("https://bioconductor.org/biocLite.R")
if(!file.exists(myf)){
if(!require("PharmacoGx")){biocLite("PharmacoGx");library(PharmacoGx)}
if(!require("Biobase")){biocLite("Biobase");library(Biobase)}
#downloadPSet("CCLE", saveDir="data")
load("../data/CCLE_hs.RData", verbose=TRUE)
#downloadPSet("GDSC", saveDir="data")
load("../data/GDSC.RData", verbose=TRUE)
###remove noisy cases and those with not matching snp profiles
snp.outliers <- c(
"LC-1F",
"HCC1937",
"MDA-MB-468",
"HuH-7",
"SW403",
"COR-L51",
"MOG-G-CCM",
"NB4")
ccle.filter <- filterNoisyCurves(CCLE, nthread=detectCores())
lapply(ccle.filter, length)#123 noisy#11547 ok
gdsc.filter <- filterNoisyCurves(GDSC, nthread=detectCores())
lapply(gdsc.filter, length)#$noisy 2315 #ok 77588
CCLE@sensitivity$info <- CCLE@sensitivity$info[ccle.filter$ok, ]
CCLE@sensitivity$raw <- CCLE@sensitivity$raw[ccle.filter$ok, , ]
CCLE@sensitivity$profiles <- CCLE@sensitivity$profiles[ccle.filter$ok, ]
GDSC@sensitivity$info <- GDSC@sensitivity$info[gdsc.filter$ok, ]
GDSC@sensitivity$raw <- GDSC@sensitivity$raw[gdsc.filter$ok, , ]
GDSC@sensitivity$profiles <- GDSC@sensitivity$profiles[gdsc.filter$ok, ]
match(snp.outliers, PharmacoGx::cellNames(CCLE))
ccle.cells <- setdiff(PharmacoGx::cellNames(CCLE), snp.outliers)
CCLE <- PharmacoGx::subsetTo(pSet=CCLE, cells=ccle.cells)
save(CCLE, file="../data/CCLE_hs_clarified.RData")
gdsc.cells <- setdiff(PharmacoGx::cellNames(GDSC), snp.outliers)
GDSC <- PharmacoGx::subsetTo(pSet=GDSC, cells=gdsc.cells)
save(GDSC, file="../data/GDSC_clarified.RData")
if(training.method== "ccle_gdsc") {
##Restrict analyses to common cells
# common <- PharmacoGx::intersectPSet(pSets=list("CCLE"=CCLE, "GDSC"=GDSC), strictIntersect=FALSE)
# cells <- intersect(common$CCLE@cell$cellid, pData(common$CCLE@molecularProfiles$isoforms)[, "cellid"])
# common$CCLE@molecularProfiles$rnaseq <- CCLE@molecularProfiles$rnaseq[, rownames(pData(CCLE@molecularProfiles$isoforms))]
# CCLE <- common$CCLE
# GDSC <- common$GDSC
drugs <- intersect(PharmacoGx::drugNames(CCLE), PharmacoGx::drugNames(GDSC))
}
ccle.drug.sensitivity <- t(PharmacoGx::summarizeSensitivityProfiles(pSet=CCLE, sensitivity.measure=as.character(sensitivity.method), drugs=drugs))
gdsc.drug.sensitivity <- t(PharmacoGx::summarizeSensitivityProfiles(pSet=GDSC, sensitivity.measure=as.character(sensitivity.method), drugs=drugs))
if(as.character(sensitivity.method)== "slope_recomputed") {
##manual cutoff for sensitivity calls based on slope
cutoff <- 0.27
res.weight <- as.integer(args[4]); sens.weight <- as.integer(args[5]);
#res.weight <- 1;sens.weight <- 10
ccle.drug.sensitivity.wighted <- ccle.drug.sensitivity
ccle.drug.sensitivity.wighted[ccle.drug.sensitivity.wighted < cutoff] <- res.weight
ccle.drug.sensitivity.wighted[ccle.drug.sensitivity.wighted >= cutoff] <- sens.weight
gdsc.drug.sensitivity.wighted <- gdsc.drug.sensitivity
gdsc.drug.sensitivity.wighted[gdsc.drug.sensitivity.wighted < cutoff] <- res.weight
gdsc.drug.sensitivity.wighted[gdsc.drug.sensitivity.wighted >= cutoff] <- sens.weight
ccle.drug.sensitivity <- ccle.drug.sensitivity.wighted * ccle.drug.sensitivity
gdsc.drug.sensitivity <- gdsc.drug.sensitivity.wighted * gdsc.drug.sensitivity
}
ccle.tissuetype <- as.data.frame(CCLE@cell[, "tissueid"], row.names=CCLE@cell[, "cellid"])
colnames(ccle.tissuetype) <- "tissue.type"
tissueTypes <- ccle.tissuetype
###
features <- fData(CCLE@molecularProfiles$rnaseq)[,"EnsemblGeneId"]
features <- intersect(fData(CCLE@molecularProfiles$isoforms)[,"EnsemblGeneId"], features)
rnaseq.features <- rownames(fData(CCLE@molecularProfiles$rnaseq))[which(fData(CCLE@molecularProfiles$rnaseq)[,"EnsemblGeneId"] %in% features)]
isoforms.features <- rownames(fData(CCLE@molecularProfiles$isoforms))[which(fData(CCLE@molecularProfiles$isoforms)[,"EnsemblGeneId"] %in% features)]
ccle.genes.fpkm <- t(Biobase::exprs(PharmacoGx::summarizeMolecularProfiles(pSet=CCLE, mDataType="rnaseq", features=rnaseq.features, fill.missing=FALSE)))
ccle.isoforms.fpkm <- t(Biobase::exprs(PharmacoGx::summarizeMolecularProfiles(pSet=CCLE, mDataType="isoforms", features=isoforms.features, fill.missing=FALSE)))
#ccle.isoforms.fpkm[which(is.na(ccle.isoforms.fpkm))] <- 0
# if(RNA_seq.normalize == FALSE)
# {
# ccle.genes.fpkm <- 2 ^ ccle.genes.fpkm - 1
# ccle.isoforms.fpkm <- 2 ^ ccle.isoforms.fpkm -1
# }
if(RNA_seq.normalize == TRUE)
{
ccle.genes.fpkm <- log2(ccle.genes.fpkm + 1)
ccle.isoforms.fpkm <- log2(ccle.isoforms.fpkm + 1)
}
ccle.cells <- intersectList(rownames(ccle.drug.sensitivity), rownames(ccle.genes.fpkm), rownames(ccle.isoforms.fpkm))
gdsc.cells <- intersectList(rownames(gdsc.drug.sensitivity), rownames(ccle.genes.fpkm), rownames(ccle.isoforms.fpkm))
genes <- colnames(ccle.genes.fpkm)
if(subset.genes){
if(subset.genes.method == "biotype"){
### subsetting genes to remove pseudogenes
ff <- fData(CCLE@molecularProfiles$rnaseq)
genes <- as.character(ff[grep("pseudogene", ff$GeneBioType, invert=TRUE), "EnsemblGeneId"])
#genes <- as.character(ff[grep("protein_coding", ff$GeneBioType), "EnsemblGeneId"])
}else{ #subset.genes.method == "expression.cut.off"
exprs.cut.off <- 0.1 * nrow(ccle.genes.fpkm)
xx <- apply(ccle.genes.fpkm, MARGIN=2, function(x){length(which(x > 0))})
genes.expressed <- names(xx)[which(xx > exprs.cut.off)]
length(genes.expressed) #46467
tt <- apply(ccle.isoforms.fpkm, MARGIN=2, function(x){length(which(x > 0))})
gg <- fData(CCLE@molecularProfiles$isoforms)[match(names(tt), fData(CCLE@molecularProfiles$isoforms)[, "EnsemblTranscriptId"]), "EnsemblGeneId"]
aa <- table(tt, gg)
xx <- apply(aa, MARGIN=2, function(x){max(which(x > 0)) - 1})
isoforms.expressed <- names(xx)[which(xx > exprs.cut.off)]
length(isoforms.expressed) #30633
genes <- intersect(genes.expressed, isoforms.expressed)
length(genes) #30633
##extra checking
isoforms.of.remained.genes <- fData(CCLE@molecularProfiles$isoforms)[which(fData(CCLE@molecularProfiles$isoforms)[, "EnsemblGeneId"] %in% genes), "EnsemblTranscriptId"]
tt <- tt[isoforms.of.remained.genes]
isoforms <- names(tt)[which(tt > exprs.cut.off)]
length(isoforms)#124314
}
}
ccle.genes.fpkm <- ccle.genes.fpkm[, genes, drop=FALSE]
ccle.isoforms.fpkm <- ccle.isoforms.fpkm[, isoforms, drop=FALSE]
GeneList <- genes
#dim(ccle.genes.fpkm) 925 35638
#dim(ccle.isoforms.fpkm) 925 140331
## for logistic regression
if(glm.family == "binomial")
{
for(c in colnames(ccle.drug.sensitivity)){ccle.drug.sensitivity[,c] <- factor(ccle.drug.sensitivity[, c])}
for(c in colnames(gdsc.drug.sensitivity)){gdsc.drug.sensitivity[,c] <- factor(gdsc.drug.sensitivity[, c])}
}
annot.ensembl.all.genes <- Biobase::fData(CCLE@molecularProfiles$rnaseq)[genes, ]
annot.ensembl.all.isoforms <- Biobase::fData(CCLE@molecularProfiles$isoforms)[isoforms, ]
ccle.cell.profiles <- CCLE@cell
save(drugs, ccle.drug.sensitivity, gdsc.drug.sensitivity, tissueTypes, ccle.genes.fpkm, ccle.isoforms.fpkm, ccle.cells, gdsc.cells, GeneList, annot.ensembl.all.genes, annot.ensembl.all.isoforms, ccle.cell.profiles, file=myf)
}else{
load(myf, verbose=TRUE)
}
startIndex <- as.integer(args[1])
finishIndex <- as.integer(args[2])
pvalues <- list()
statistics <- list()
best.isoforms <- list()
pvalues.r.squared <- pvalues.cindex <- pvalues
statistics.r.squared <- statistics.cindex <- statistics
for(i in startIndex: finishIndex)
{
#Gene <- "ENSG00000171094" ALK drugs <- c("TAE684", "Crizotinib", "PLX4720", "lapatinib", "Erlotinib") expression is supposed to be predictive to TAE684
#Gene <- ENSG00000171094 ALK c("TAE684", "Crizotinib", "PLX4720", "lapatinib", "Erlotinib") expression is supposed to be predictive to Crizotinib
#Gene <- "ENSG00000157764" BRAF drugs <- c("TAE684", "Crizotinib", "PLX4720", "lapatinib", "Erlotinib") mutation is supposed to be predictive to PLX4720
#Gene <- "ENSG00000141736" ERBB2 drugs <- c("TAE684", "Crizotinib", "PLX4720", "lapatinib", "Erlotinib") expression is supposed to be predictive to lapatinib
#Gene <- "ENSG00000146648" EGFR drugs <- c("TAE684", "Crizotinib", "PLX4720", "lapatinib", "Erlotinib") expression is supposed to be predictive to Erlotinib
#Gene <- "ENSG00000146648" EGFR drugs <- c("TAE684", "Crizotinib", "PLX4720", "lapatinib", "Erlotinib") expression is supposed to be predictive to Erlotinib
#Gene <- "ENSG00000181019" "1728" #NQO1 "3480" #IGF1R
#Gene <- "ENSG00000148426" "C10orf47"
#MicroArrayExp <- ccle.drug.microarray.exp[,Gene]; Gene_FPKM <- ccle.genes.fpkm[,Gene]; Isoforms <- fnIsoformsExp(Isoforms_FPKM=ccle.isoforms.fpkm, GeneId=Gene); GeneID <- Gene; model.method <- "npreg"; method <- "bootstrap"; effect.size <- "adj.r.squared";glm.family <- "gaussian"; tissue <- NULL; sample.no.threshold <- 5; assay <- "ccle"
#which(fData(common$CCLE@molecularProfiles$rnaseq)[, "EntrezGeneId"]=="1728") #18024
Gene <- as.character(GeneList[i])
#print(Gene)
if(training.method == "ccle_gdsc")
{
sensitivity <- fnSensitivityCompare(MicroArrayExp=NULL,
Gene_FPKM=ccle.genes.fpkm[,Gene],
Isoforms=fnIsoformsExp(Isoforms_FPKM=ccle.isoforms.fpkm, GeneId=Gene),
GeneID=Gene,
drugs=drugs,
effect.size=effect.size)
}else if(training.method == "gCSI"){
sensitivity <- fnSensitivityOneDataSet(MicroArrayExp=NULL,
Gene_FPKM= gCSI.genes.fpkm[,Gene],
Isoforms=fnIsoformsExp(Isoforms_FPKM=gCSI.isoforms.fpkm, GeneId=Gene),
GeneID=Gene,
assay=training.method,
effect.size=effect.size)
}else{
sensitivity <- fnSensitivityOneDataSet(MicroArrayExp=NULL,
Gene_FPKM= ccle.genes.fpkm[,Gene],
Isoforms=fnIsoformsExp(Isoforms_FPKM=ccle.isoforms.fpkm, GeneId=Gene),
GeneID=Gene,
assay=training.method,
effect.size=effect.size)
}
if(effect.size == "r.squared & cindex") {
pvalues.r.squared[[Gene]] <- sensitivity$p.values.r.squared
pvalues.cindex[[Gene]] <- sensitivity$p.values.cindex
statistics.r.squared[[Gene]] <- sensitivity$statistics.r.squared
statistics.cindex[[Gene]] <- sensitivity$statistics.cindex
best.isoforms[[Gene]] <- sensitivity$best.isoforms
}else{
pvalues[[Gene]] <- sensitivity$p.values
statistics[[Gene]] <- sensitivity$statistics
best.isoforms[[Gene]] <- sensitivity$best.isoforms
}
print(sprintf("Models of Gene %s [%d] is built at %s", Gene, i, Sys.time()))
}
if(effect.size == "r.squared & cindex") {
both.drug.association.adj.r.squared.pvalues <- list("r.squared"=pvalues.r.squared, "cindex"=pvalues.cindex)
both.drug.association.statistics <- list("r.squared"=statistics.r.squared, "cindex"=statistics.cindex)
}else{
both.drug.association.adj.r.squared.pvalues <- pvalues
both.drug.association.statistics <- statistics
}
both.drug.association.best.isoforms <- best.isoforms
path.result <- file.path(output.folder)
if (!file.exists(path.result)){dir.create(file.path(path.result))}
save(both.drug.association.adj.r.squared.pvalues, both.drug.association.statistics,both.drug.association.best.isoforms, file=file.path(path.result ,paste0(paste(as.character(startIndex),as.character(finishIndex),sep="_"), ".RData")))