##
# testKnownDrugs
#
# This script iterates through a set of high-throughput screens with
# matching expression data and compares the ability of this approach
# to identify known drugs that affect cell viability
##
library(fendR)
library(plyr)
#' \code{findDrugsWithTargetsAndGenes} Identifies drugs in a
#' @param eset.file Expression set with expression and phenotype data
#' @param viper.file Viper file with networks for all phenotypes
#' @param w
#' @param b
#' @param mu
#' @keywords
#' @export
#' @examples
#' @return list of network result objects
#'
findDrugsWithTargetsAndGenes <-function(eset.file,
viper.file,
genotype='nf1 genotype',
conditions=list(homozygous=list(WT="+/+",KO="-/-"),
KOvsHets=list(WT=c("+/+","+/-"),KO="-/-"),
InclHets=list(WT="+/+",KO=c("+/-","-/-"))),
w=2,
b=1,
mu=5e-04){
library(synapser)
synLogin()
require(parallel)
require(Biobase)
# cl <- makeCluster(nnodes=8)
eset<-readRDS(synGet(eset.file)$path)
pset<-fendR::addGenotypeClass(eset,conditions,genotype)
#get drugs that have target ids
# matched.ids <- getDrugIds(varLabels(pset))
# tested.drugs <- matched.ids$ids
#print(matched.ids)
# if(!missing(drug.name)){
# inds <- which(tolower(matched.ids$drugs)%in%tolower(drug.name))
# if(length(inds)>0)
# matched.ids<-matched.ids[inds,]
# }
library(viper)
v.obj <- readRDS(synapser::synGet(viper.file)$path)
# matched.drugs <- which(sapply(toupper(varLabels(pset)),function(x) unlist(strsplit(x,split='_'))[1])%in%matched.ids$drugs)
#get those with significantly differentially expressed genes
all.vprots<-lapply(names(conditions),function(cond){
wt = which(Biobase::pData(pset)[[cond]] =='WT')
ko= which(Biobase::pData(pset)[[cond]]=='KO')
wt.names=intersect(colnames(v.obj),Biobase::pData(pset)$Sample[wt])
ko.names=intersect(colnames(v.obj),Biobase::pData(pset)$Sample[ko])
# print(paste("found",length(high),'high and',length(low),'low samples for',drug,sep=' '))
res<-fendR::getViperForDrug(v.obj,wt.names,ko.names,0.001,TRUE,FALSE)
print(paste("Found ",paste(names(res),collapse=','),' for condition ',cond))
return(res)
})
names(all.vprots)<-names(conditions)
# all.pvals<-sapply(tolower(matched.ids$drugs),function(drug) viper::rowTtest(pset, pheno=drug,group1='High',group2='Low')$p.value)
# sig.genes<-apply(all.pvals,2,function(x) length(which(p.adjust(x)<0.05)))
nz.sig<-which(sapply(all.vprots,length)>5)
print(paste("found",length(nz.sig),'drugs at least 5 differentially expressed prots'))
#build network
drug.graph <- fendR::loadDrugGraph()
combined.graph <-fendR::buildNetwork(drug.graph)
all.drugs <- fendR::getDrugsFromGraph(drug.graph)
dids<-as.character(getDrugIds(names(pData(eset)),split='_')[,1])
fname=paste(paste(eset.file,viper.file,w,b,mu,sep='_'),'.rds',sep='')
#print(names(all.vprots)[nz.sig])
#TODO: make this multi-core, possibly break into smaller functions
all.res <- lapply(names(all.vprots)[nz.sig],function(cond,all.vprots,w,b,mu,fname,conditions){
#create viper signature from high vs. low
cat(cond)
#print(high)
v.res=all.vprots[[cond]]
newf=paste(cond,fname,sep='_')
if(file.exists(newf)){
pcsf.res<-readRDS(newf)
} else{
# print(v.res)
pcsf.res.id <-fendR::runPcsfWithParams(ppi=combined.graph,terminals=abs(v.res),dummies=dids,w=w,b=b,mu=mu,doRand=TRUE)
pcsf.res <-fendR::renameDrugIds(pcsf.res.id,dids)
saveRDS(pcsf.res,file=newf)
}
drug.res <- igraph::V(pcsf.res)$name[which(igraph::V(pcsf.res)$type=='Compound')]
cat(paste("Selected",length(drug.res),'drugs in the graph'))
pvalsAndFigs=plotDrugs(eset.file,drug.res,genotype)
tab<-do.call(cbind,vertex.attributes(pcsf.res))
ttab<-as.data.frame(tab)%>%filter(type=='Compound')%>%mutate(Drug=tolower(name))%>%dplyr::select(c(Drug,prize))
#now we need to store all of these in the updated table.
res=left_join(pvalsAndFigs,ttab,by='Drug')
##collect stats, store in synapse table
list(network=pcsf.res,
drugs=drug.res,
w=w,
b=b,
mu=mu,
ko=paste(conditions[[cond]]$KO,collapse=','),
wt=paste(conditions[[cond]]$WT,collapse=','),
viperProts=names(v.res),
# inputDrug=unlist(strsplit(drug,split='_'))[1],
file=newf,
compoundStats=res)
},all.vprots,w=w,b=b,mu=mu,fname,conditions)#,mc.cores=28)#.parallel=TRUE,.paropts = list(.export=ls(.GlobalEnv)))
names(all.res)<-names(all.vprots)[nz.sig]
all.res
}
#'
#'trackNetworkStats takes a list of results from the drug test and shares them on synapse
#'@param pcsf.res.list
#'@param synTableId
#'@param esetFileId
#'@param viperFileId
#'
trackNetworkStats<-function(pcsf.res.list,synTableId='syn16780706',esetFileId,viperFileId,dsetName='', pcsf.parent='syn15734434', plot.parent='syn15734433'){
require(synapser)
this.script='https://github.com/Sage-Bionetworks/fendR/blob/master/dev/byGenotypeNF1/testNF1_allStatus.R'
#decouple pcsf.res.list into data frame
# require(doMC)
# cl <- makeCluster(nnodes=8)
require(parallel)
# registerDoMC(cores=28)
fin<-lapply(pcsf.res.list,function(x){
#first store network
network=x[['network']]
w=x[['w']]
b=x[['b']]
mu=x[['mu']]
fname=x[['file']]
ko=x[['ko']]
wt=x[['wt']]
ds=x[['compoundStats']]%>%rename(Drug='Selected Drug',p.value='Drug Wilcoxon P-value')%>%mutate('Drug Prize Value'=as.numeric(prize))%>%ungroup()
ds$`Drug Boxplot`=sapply(ds$figFile,function(y) synStore(File(y,parentId=plot.parent))$properties$id)
res=synapser::synStore(File(fname,parentId=pcsf.parent),used=c(esetFileId,viperFileId),executed=this.script)
ds=ds%>%dplyr::select(-figFile,-prize)
#store image file
upl<-data.frame(`NF1 KO`=ko,`NF1 WT`=wt,w=w,beta=b,mu=mu,
`Viper Proteins`=paste(sort(x$viperProts),collapse=','),
`Original eSet`=esetFileId,`Original metaViper`=viperFileId,
`PCSF Result`=res$properties$id,`Dataset name`=dsetName,check.names=F)#,
# check.names=F)
upl2=merge(ds,upl)
tres<-synapser::synStore(Table(synTableId,upl2))
})#,mc.cores=28)
#.parallel=TRUE,.paropts = list(.export=ls(.GlobalEnv)))
# stopCluster(cl)
#store as synapse table
}
####ntap files
#synIds<-list(NTAP=list(results='syn12333924',eset.file='syn12333863',viper.file='syn12333867',tableId='syn12334021'),
synIds=list(
CCLE=list(eset.file='syn12549491',viper.file='syn12549589'),
Sanger=list(eset.file='syn12549635',viper.file='syn12549806'))
wvals=c(2,3,4,5)
bvals=c(1,2,5,10)
muvals=c(5e-05,5e-04,5e-03,5e-02)
#for(w in c(2,3,4,5)){
# for(b in c(1,2,5,10)){
# for(mu in c()){
all.params=expand.grid(w=wvals,b=bvals,mu=muvals,dname=names(synIds))
#all.params=all.params[1:10,]
fr=mdply(.data=all.params,.fun=function(w,b,mu,dname){
x=synIds[[dname]]
all.res<-findDrugsWithTargetsAndGenes(eset.file=x$eset.file,
viper.file=x$viper.file,
genotype='nf1',
conditions=list(KOvsWT=list(KO=1,WT=0)),
w=w,b=b,mu=mu)
trackNetworkStats(all.res,esetFileId=x$eset.file,viperFileId=x$viper.file, dsetName=dname)
},.parallel=TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.