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dermalNFData.R
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dermalNFData.R
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###
## dermalNFData.R
## This file is designed to be a basic library file so that a user can collect a specific type of dermalNF data
## Author: Sara Gosline
## Email: [email protected]
###
library(synapseClient)
synapseLogin()
library(data.table)
require(parallel)
#################
# CNV
#################
cnv_annotations<-function(){
snpfiles=synapseQuery('SELECT id,name,patientID,tissueType,tissueID,alternateTumorID FROM entity where parentId=="syn5004874"')
names(snpfiles)<-c('tissueType','patientId','alternateTumorId','File','tissueId','synapseId')
snpfiles=snpfiles[which(!is.na(snpfiles$patientId)),]
return(snpfiles)
}
cnv.dat<-cnv_annotations()
cnv.dat<-cnv.dat[which(!is.na(cnv.dat$patientId)),]
patients<-cnv.dat$patientId
names(patients)<-sapply(cnv.dat$File,function(x) gsub('3096-PBK-','X',gsub('_Final.csv','',x)))
patients<-sapply(patients,function(x) gsub("CT0*","",x))
names(patients)<-cnv.dat$synapseId
clnames<-paste(patients,cnv.dat$tissueId)
names(clnames)<-names(patients)
tissueType=cnv.dat$tissueType
names(tissueType)<-names(patients)
#SNP annotation file
snp_annotation_file<-function(){
##need to downlod and read in large annotation file as well
print("Retrieving OMNI Array SNP annotation data from Synapse...")
anndata<-synGet('syn5297573')
return(anndata@filePath)
}
snp_annotation_data<-function(){
fp=snp_annotation_file()
annot <- as.data.frame(fread(fp,sep=",",header=T))
return(annot)
}
cnv_unprocessed_files<-function(){
snpfiles=synapseQuery('SELECT id,name,patientID,tissueType,tissueID FROM entity where parentId=="syn5004874"')
snpfiles<-snpfiles[grep("Final.csv",snpfiles$entity.name),]
snp.sample.names<-sapply(snpfiles$entity.name,function(x) gsub('_Final.csv','',unlist(strsplit(x,split='-'))[3]))
snp.patients<-snpfiles$entity.patientID
names(snp.patients)<-snp.sample.names
snp.tissue<-snpfiles$entity.tissueID
names(snp.tissue)<-snp.sample.names
if(require(parallel))
lapply<-function(x,...) mclapply(x,...,mc.cores=4)
sample.data<-lapply(snpfiles$entity.id,function(synid){
print(paste("Getting sample",snpfiles$entity.name[match(synid,snpfiles$entity.id)]))
fname=synGet(synid)
return(fname@filePath)
})
names(sample.data)<-snpfiles$entity.id
return(sample.data)
}
##this processes gets the ASCAT segmented data
ascat_segments<-function(recalc=FALSE,annot=NA,byval='gene',metric='median'){
require(DNAcopy)
require(CNTools)
if(!recalc){
return(list(LRR=lrr.segM,BAF=baf.segM))
}
if(is.na(annot))
annot=snp_annotation_data()
f=synGet("syn6182422")@filePath
unzip(f)
allfiles=list.files('./output_aspcf/all')
##now we have to do some file name munging to get patient data
##then merge all files together
is.autosome <- as.character(annot$Chr) %in% as.character(1:22)
auto.annot<-annot[is.autosome,]
#map lrr and bafs for each
lrr.files<-allfiles[grep("LogR",allfiles)]
lrr.samps<-sapply(lrr.files,function(x) unlist(strsplit(x,split='.',fixed=T))[1])
baf.files<-allfiles[grep('BAF',allfiles)]
baf.samps<-sapply(baf.files,function(x) unlist(strsplit(x,split='.',fixed=T))[1])
##now collect mapping info to get patient/sample numbers...
mapping<-read.table(synGet('syn4999547')@filePath,header=T,sep='\t')
if(!exists("geneInfo"))
geneInfo<-read.table('../../data/hg19_geneInfo.txt')
##download all lrr files, read in
lrr <- do.call("cbind", lapply(lrr.files, function(x) {
tab<-read.table(paste('output_aspcf/all/',x,sep=''))
idx<-match(auto.annot$Name,tab[,1])
na.idx<-idx[which(!is.na(idx))]
t2<-tab[na.idx,2]
names(t2)<-auto.annot$Name[which(!is.na(idx))]
return(t2)}))
lrr.pats<-sapply(as.character(mapping$Patient.ID[match(lrr.samps,mapping$Sample.ID)]),function(x){
ps<-unlist(strsplit(x,split=' '))
pat<-gsub('CT0+','',ps[1])
samp<-gsub('0+','',ps[2])
return(paste('Patient',pat,'DNASample',samp,sep='_'))
})
colnames(lrr)<-lrr.pats
##use CN Tools to agglomerate the data, though maybe not segment it?
##START WITH LRR
matched.annot<-auto.annot[which(auto.annot$Name%in%rownames(lrr)),]
cna <- CNA(lrr,matched.annot$Chr, matched.annot$Map,data.type='logratio',lrr.pats)
smoothed.cna <- smooth.CNA(cna)
segment.smoothed.cna <- segment(smoothed.cna, verbose=1)
lrr.seg<-segment.smoothed.cna$output
cs<-CNSeg(lrr.seg)
rdseg <- getRS(cs, by = byval,geneMap=geneInfo, imput = FALSE, XY = FALSE, what =metric)
lrr.segM <- rs(rdseg)
##doanload all BAF files, read in
baf <- do.call("cbind", lapply(baf.files, function(x){
tab<- read.table(paste('output_aspcf/all/',x,sep=''))
idx<-match(auto.annot$Name,tab[,1])
na.idx<-idx[which(!is.na(idx))]
t2<-tab[na.idx,2]
names(t2)<-auto.annot$Name[which(!is.na(idx))]
return(t2)}))
baf.pats<-sapply(as.character(mapping$Patient.ID[match(baf.samps,mapping$Sample.ID)]),function(x){
ps<-unlist(strsplit(x,split=' '))
pat<-gsub('CT0+','',ps[1])
samp<-gsub('0+','',ps[2])
return(paste('Patient',pat,'DNASample',samp,sep='_'))
})
colnames(baf)<-baf.pats
##use CN Tools to agglomerate the data, though maybe not segment it?
##START WITH LRR
matched.annot<-auto.annot[which(auto.annot$Name%in%rownames(baf)),]
cna <- CNA(baf,matched.annot$Chr, matched.annot$Map,data.type='logratio',baf.pats)
smoothed.cna <- smooth.CNA(cna)
segment.smoothed.cna <- segment(smoothed.cna, verbose=1)
baf.seg<-segment.smoothed.cna$output
cs<-CNSeg(baf.seg)
rdseg <- getRS(cs, by = byval,geneMap=geneInfo, imput = FALSE, XY = FALSE, what =metric)
baf.segM <- rs(rdseg)
return(list(LRR=lrr.segM,BAF=baf.segM,LRR.seg=lrr.seg,BAF.seg=baf.seg))
}
#this function gets the original files from the OMNI arrays
cnv_unprocessed<-function(annot=NA){
if(is.na(annot))
annot=snp_annotation_data()
##SNP data files
snpfiles=synapseQuery('SELECT id,name,patientID,tissueType,tissueID FROM entity where parentId=="syn5004874"')
snpfiles<-snpfiles[grep("Final.csv",snpfiles$entity.name),]
snp.sample.names<-sapply(snpfiles$entity.name,function(x) gsub('_Final.csv','',unlist(strsplit(x,split='-'))[3]))
snp.patients<-snpfiles$entity.patientID
names(snp.patients)<-snp.sample.names
snp.tissue<-snpfiles$entity.tissueID
names(snp.tissue)<-snp.sample.names
print('Now retreiving original CNV data from Dermal NF OMNI arrays...')
#here get the sample data from snp files
sample.data<-lapply(snpfiles$entity.id,function(synid){
print(paste("Getting sample",snpfiles$entity.name[match(synid,snpfiles$entity.id)]))
fname=synGet(synid)
data <- as.data.frame(fread(fname@filePath,sep=",",header=T))
ad<-data[match(annot$Name,data$'SNP.Name'),]
return(ad)
})
names(sample.data)<-snpfiles$entity.id
return(sample.data)
}
#this gets the CNV segment files
cnv_segmented<-function(filterSD=TRUE){
if(filterSD)
si='syn5049753'
else
si='syn5049755'
fn<-synGet(si)
tab<-read.table(fn@filePath,header=T)
return(tab)
}
cnv_segmented_by_gene<-function(){
si='syn5462050'
fn<-synGet(si)
tab<-read.table(fn@filePath,header=T)
return(tab)
}
cnv_segmented_by_region<-function(){
si='syn5462067'
fn<-synGet(si)
tab<-read.table(fn@filePath,header=T)
return(tab)
}
#################
#PROTEOMICS
#################
protein_annotations<-function(){
annots<-synapseQuery("select name,ID,dataType,tissueID,tissueType,patientID,sampleID from entity where parentId=='syn4984949'")
annots<-annots[-grep('EMPTY',annots$entity.name),]
colnames(annots)<-c('tissueType','dataType','sampleId','patientId','fileName','tissueId','synapseId')
return(annots)
}
#this merely calculates the ratio for each file
get.protein.from.file<-function(sn,top_only=FALSE){
sd<-synGet(unlist(sn))
tab<-read.table(sd@filePath,header=T,as.is=T,quote='"')
nums<-tab[,6]
denoms<-tab[,7]
ratios<-nums/denoms
groups<-tab[,1]
u.groups<-unique(groups)
groups.ids<-sapply(u.groups,function(x) return(paste(unique(tab[which(tab[,1]==x),4]),collapse=';')))
names(groups.ids)<-u.groups
print(paste("Found",length(u.groups),'unique protein groups'))
u.tops<-sapply(u.groups,function(x) intersect(which(tab[,1]==x),which(tab[,3]=='TOP PROTEIN')))
#now filter for top
top.ratios=ratios[u.tops]
top.nums=nums[u.tops]
top.conts=denoms[u.tops]
return(list(Ratios=top.ratios,Raw=top.nums,Control=top.conts,Prot.ids=tab[u.tops,4],Origin=tab[u.tops,8]))
}
prot_unnormalized<-function(){
allfiles= synapseQuery('SELECT name,ID,patientID,tissueID,originalBatch FROM entity WHERE parentId=="syn4984949"')
res<-sapply(allfiles$entity.id,function(x) get.protein.from.file(x,TRUE))
# names(res)<-allfiles$entity.id
#first col lect all proteins annotated in any file
all.prots<-NULL
for(i in 1:ncol(res))
all.prots<-union(all.prots,res[['Prot.ids',i]])
#filter for those that are expressed across all samples
# expr.prots<-res[['Prot.ids',1]]
# for(i in 2:ncol(res))
# expr.prots<-intersect(expr.prots,res[['Prot.ids',i]])
prot.ids<-unique(unlist(sapply(all.prots,function(x) unlist(strsplit(x,split=';')))))
#now create biomart mapping
require(biomaRt)
ensembl=useMart("ENSEMBL_MART_ENSEMBL",dataset="hsapiens_gene_ensembl",host='www.ensembl.org')
filters = listFilters(ensembl)
attributes = listAttributes(ensembl)
epep="ensembl_peptide_id"
egene='hgnc_symbol'
gene.mapping<-getBM(attributes=c(epep,egene),filters=c(epep),values=as.list(prot.ids),mart=ensembl)
allsamps<-colnames(res)
sfiles=sapply(allsamps,function(x) res[['Origin',x]][1])
expr.ratio.mat<-sapply(all.prots,function(x){
pvec<-sapply(allsamps,function(i){
rv<-grep(x,res[['Prot.ids',i]])
if(length(rv)==0)
return(0)
else
return(res[['Ratios',i]][rv])
})
names(pvec)<-allsamps
unlist(pvec)
})
expr.raw.mat<-sapply(all.prots,function(x){
# pvec<-NULL
# samps<-NULL
pvec<-sapply(allsamps,function(i){
rv<-grep(x,res[['Prot.ids',i]])
if(length(rv)==0)
return(0)
else
return(res[['Raw',i]][rv])
})
names(pvec)<-allsamps
unlist(pvec)
})
gn<-gene.mapping[match(colnames(expr.ratio.mat),gene.mapping[,1]),2]
expr.ratio.mat[which(is.na(expr.ratio.mat),arr.ind=T)]<-0.0
#expr.ratio.mat<-expr.ratio.mat[-grep('EMPTY',rownames(expr.ratio.mat)),]
gn<-gene.mapping[match(colnames(expr.ratio.mat),gene.mapping[,1]),2]
gn[which(is.na(gn))]<-colnames(expr.ratio.mat)[which(is.na(gn))]
colnames(expr.ratio.mat)<-gn
gn<-gene.mapping[match(colnames(expr.raw.mat),gene.mapping[,1]),2]
expr.raw.mat[which(is.na(expr.raw.mat),arr.ind=T)]<-0.0
#expr.ratio.mat<-expr.ratio.mat[-grep('EMPTY',rownames(expr.ratio.mat)),]
gn<-gene.mapping[match(colnames(expr.raw.mat),gene.mapping[,1]),2]
gn[which(is.na(gn))]<-colnames(expr.raw.mat)[which(is.na(gn))]
colnames(expr.raw.mat)<-gn
##now create a regular comparison of each sample, protein, and control, patient
ratios=tidyr::gather(data.frame(Sample=rownames(expr.ratio.mat),expr.ratio.mat),"Protein","Ratio",1+1:ncol(expr.ratio.mat))
raws=tidyr::gather(data.frame(Sample=rownames(expr.raw.mat),expr.raw.mat),"Protein","RawValue",1+1:ncol(expr.raw.mat))
patients=sapply(allfiles$entity.patientID[match(raws$Sample,allfiles$entity.id)],function(x) gsub("CT0+","",x))
tids=paste("Patient",patients,'Tissue',allfiles$entity.tissueID[match(raws$Sample,allfiles$entity.id)],sep='_')
experiments=sapply(allfiles$entity.originalBatch[match(raws$Sample,allfiles$entity.id)],function(x) unlist(strsplit(x,split='_'))[2])
full.df=data.frame(ratios,RawValue=raws$RawValue,Tissue=tids,Patient=patients,Experiment=experiments)
mindf=subset(full.df,Tissue!='Patient_NULL_Tissue_NULL')
ggplot(mindf)+geom_boxplot(aes(x=Experiment,y=Ratio,fill=Tissue))+scale_y_log10()
return(mindf)
}
prot_normalized<-function(store=FALSE,all.expr=TRUE){
#store indicates we should calculate the values and uplod to synapse, otherwise we can just download pre-computed
#all.expr means select only those proteins that non-zero in at least one sample
allfiles= synapseQuery('SELECT name,ID,patientID,tissueID FROM entity WHERE parentId=="syn4984949"')
if(store){
res<-sapply(allfiles$entity.id,function(x) get.protein.from.file(x,TRUE))
names(res)<-allfiles$entity.id
#first col lect all proteins annotated in any file
all.prots<-NULL
for(i in 1:ncol(res))
all.prots<-union(all.prots,res[['Prot.ids',i]])
#filter for those that are expressed across all samples
# expr.prots<-res[['Prot.ids',1]]
# for(i in 2:ncol(res))
# expr.prots<-intersect(expr.prots,res[['Prot.ids',i]])
prot.ids<-unique(unlist(sapply(all.prots,function(x) unlist(strsplit(x,split=';')))))
#now create biomart mapping
require(biomaRt)
ensembl=useMart("ENSEMBL_MART_ENSEMBL",dataset="hsapiens_gene_ensembl",host='www.ensembl.org')
filters = listFilters(ensembl)
attributes = listAttributes(ensembl)
epep="ensembl_peptide_id"
egene='hgnc_symbol'
gene.mapping<-getBM(attributes=c(epep,egene),filters=c(epep),values=as.list(prot.ids),mart=ensembl)
allsamps<-colnames(res)
expr.ratio.mat<-sapply(all.prots,function(x){
# pvec<-NULL
# samps<-NULL
pvec<-sapply(allsamps,function(i){
rv<-grep(x,res[['Prot.ids',i]])
if(length(rv)==0)
return(0)
else
return(res[['Ratios',i]][rv])
})
names(pvec)<-allsamps
unlist(pvec)
})
gn<-gene.mapping[match(colnames(expr.ratio.mat),gene.mapping[,1]),2]
expr.ratio.mat[which(is.na(expr.ratio.mat),arr.ind=T)]<-0.0
#expr.ratio.mat<-expr.ratio.mat[-grep('EMPTY',rownames(expr.ratio.mat)),]
gn<-gene.mapping[match(colnames(expr.ratio.mat),gene.mapping[,1]),2]
gn[which(is.na(gn))]<-colnames(expr.ratio.mat)[which(is.na(gn))]
colnames(expr.ratio.mat)<-gn
expr.ratio.mat<-t(expr.ratio.mat)
df=data.frame(Protein=rownames(expr.ratio.mat),expr.ratio.mat)
write.table(df,file='proteinFoldChangeOverControl.txt',sep='\t',row.names=F)
print('Storing file on Synapse...')
synStore(File('proteinFoldChangeOverControl.txt',parentId='syn4984703'),
used=list(c(sapply(allfiles$entity.id,function(x) list(entity=x)),list(name='dermalNFData.R',url='https://raw.githubusercontent.com/Sage-Bionetworks/dermalNF/master/bin/dermalNFData.R'))),
activityName='Computed ratios between protein and control',
activityDescription='called prot_normalized with store=TRUE')
expr.ratio.mat<-df
}else{
matfile=synGet('syn5305003')
expr.ratio.mat<-as.data.frame(fread(matfile@filePath,sep='\t',header=T))
}
if(all.expr){
zo=which(apply(expr.ratio.mat[,-1],1,function(x) all(x==0)))
if(length(zo)>0){
print(paste('Removing',length(zo),'proteins from matrix because they have only 0-values'))
expr.ratio.mat<-expr.ratio.mat[-zo,]
}
return(expr.ratio.mat)
}
return(expr.ratio.mat)
}
#################
#RNA
#################
patient_tumor_number_rna<-function(idlist,quant='cuffLinks'){
if(tolower(quant)=='cufflinks'){
##the PBK ids are missing from table, so need to query annotations
res<-synQuery("select patientID,tissueID,sampleID from entity where parentId=='syn5492805'")
# map<-unique(res)
#from table get generic tumor id
tres<-synTableQuery("SELECT Patient,RnaID,TumorNumber,'RNASeq (Cufflinks)' FROM syn5556216 where RnaID is not NULL")@values
idx<-match(res$entity.id,tres$`RNASeq (Cufflinks)`)
dres<-res[which(!is.na(idx)),]
tres<-tres[idx[which(!is.na(idx))],]
full.map<-cbind(dres,tres)
#map tumors to sample ids
sampleIds<-sapply(idlist,function(x){
y=which(full.map$entity.sampleID==gsub('X','',gsub('.','-',x,fixed=T)))
paste("Patient",full.map$Patient[y],"Tumor",full.map$TumorNumber[y])
})
}else if(tolower(quant)=='featurecounts'){
res<-synTableQuery("SELECT Patient,TumorNumber,RNASeq FROM syn5556216 where RNASeq is not NULL")@values
sampleIds<-sapply(idlist,function(x){
y=which(res$RNASeq==gsub('”','',x))
paste("Patient",res$Patient[y],"Tumor",res$TumorNumber[y])
})
}
return(sampleIds)
}
rna_annotations<-function(){
synq=synapseQuery("select name,id,patientID,tissueID,alternateTumorID from entity where parentId=='syn5493036'")
colnames(synq)<-c('patientId','alternateTumorId','fileName','tissueId','synapseId')
synq=synq[grep('_featureCounts.txt',synq$fileName),]
return(synq)
}
rna_cufflinks_annotations<-function(){
synq=synapseQuery("select sampleID,patientID,tissueID,tissueType,alternateTumorID from entity where parentId=='syn5492805'")
colnames(synq)<-c('tissueType','patientID','sampleID','altTumorID','tissueID','synapseID')
return(synq)
}
rna_bam_annotations<-function(){
synq=synapseQuery("select name,id,patientID,tissueID,alternateTumorID from entity where parentId=='syn4984620'")
colnames(synq)<-c('patientID','alternateTumorId','fileName','tissueId','synapseId')
synq=synq[grep('.bam$',synq$fileName),]
return(synq)
}
##here are the count files analyzed by featureCounts
rna_count_matrix<-function(stored=TRUE,doNorm=FALSE,minCount=0,doLogNorm=FALSE,doVoomNorm=FALSE){
if(!stored){
synq=synapseQuery("select name,id,patientID,tissueID from entity where parentId=='syn5493036'")
synq<-synq[grep("accepted_hits",synq$entity.name),]
synfiles<-sapply(synq$entity.id,synGet)
#now read in alfilel values
allfs<-lapply(synfiles,function(x) read.table(x@filePath,header=T,as.is=T))
names(allfs)<-synq$entity.id
#now get individual genes to create data matrix
hugo.genes<-unique(allfs[[1]][,2])
#now let's get individual counts across patient samples
gene.pat.mat<-sapply(hugo.genes,function(x,allfs){
res<-sapply(names(allfs),function(y){
mat<-allfs[[y]]
sum(mat[which(mat[,2]==x),1])})
names(res)<-names(allfs)
res
},allfs)
colnames(gene.pat.mat)<-hugo.genes
write.table(gene.pat.mat,file='featureCountsByGeneBySample.txt',row.names=T,col.names=T)
sf=File('featureCountsByGeneBySample.txt',parentId='syn4984701')
synStore(sf,used=list(list(name='dermalNFData.R',
url='https://raw.githubusercontent.com/Sage-Bionetworks/dermalNF/master/bin/dermalNFData.R')),
activityName='Create matrix of all counts across samples')
}else{
gene.pat.mat<-read.table(synGet('syn5051784')@filePath)
}
gene.pat.mat<-t(gene.pat.mat)
if(doNorm){
print('Performing size factor adjustment to samples')
require(DESeq2)
samp=data.frame(SampleID=colnames(gene.pat.mat))
cds<- DESeqDataSetFromMatrix(gene.pat.mat,colData=samp,~SampleID)#now collect proteomics data
sizeFac<-estimateSizeFactors(cds)
normCounts<-assay(cds)/sizeFac@colData$sizeFactor
colnames(normCounts)<-colnames(gene.pat.mat)
gene.pat.mat<-normCounts
}else if(doLogNorm){
print("Performing variance stabilizing log2 normalization")
require(DESeq2)
samp=data.frame(SampleID=colnames(gene.pat.mat))
cds<- DESeqDataSetFromMatrix(gene.pat.mat,colData=samp,~SampleID)#now collect proteomics data
vstab=rlog(cds)
varmat<-assay(vstab)
colnames(varmat)<-colnames(gene.pat.mat)
gene.pat.mat<-varmat
minCount=log2(minCount)
}else if(doVoomNorm){
print("Performing VOOM normalization")
library(limma)
ret = voomWithQualityWeights(gene.pat.mat)$E
}
sel.vals=which(apply(gene.pat.mat,1,function(x) all(x>=minCount)))
if(doVoomNorm)
gene.pat.mat=ret
return(gene.pat.mat[sel.vals,])
}
fpkm_annotations<-function(x){
fpkm_files=synQuery("select sampleID,tissueID,patientID from entity where parentId=='syn5492805'")
colnames(fpkm_files)<-c('patient','sample','tissue','entity')
tumNum<-synTableQuery('select Patient,RnaID,TumorNumber from syn5556216 where RNASeq is not NULL')@values
fpkm_files$patient=sapply(fpkm_files$patient,function(x) gsub('CT0+','',x))
fpkm_files$sample=sapply(fpkm_files$sample,function(x) paste('X',gsub("-",'.',x),sep=''))
fpkm_files$TumorNumber=apply(fpkm_files,1,function(x) tumNum$TumorNumber[intersect(which(tumNum$Patient==x[[1]]),which(tumNum$RnaID==gsub('00','',x[[3]])))])
fpkm_files
}
#we can also get the FPKM
rna_fpkm_matrix<-function(byIsoform=FALSE){
##DOES NOT WORK YET....
if(byIsoform){
gene.pat.mat<-read.table(synGet('syn5579597')@filePath)
}
else{
gene.pat.mat<-read.table(synGet('syn5579598')@filePath,row.names=NULL)
dupes<-unique(gene.pat.mat[which(duplicated(gene.pat.mat[,1])),1])
dupe.vals<-t(sapply(dupes,function(x)
colSums(gene.pat.mat[which(gene.pat.mat[,1]==x),2:ncol(gene.pat.mat)])))
sing.vals<-gene.pat.mat[which(!gene.pat.mat[,1]%in%dupes),2:ncol(gene.pat.mat)]
rownames(dupe.vals)<-dupes
rownames(sing.vals)<-gene.pat.mat[which(!gene.pat.mat[,1]%in%dupes),1]
newdf<-rbind(dupe.vals,sing.vals)
gene.pat.mat<-newdf
}
#gene.pat.mat<-t(gene.pat.mat)
return(gene.pat.mat)
}
#################
#WGS
#################
wgs_annotations<-function(){
synq=synapseQuery("select name,id,patientID,tissueID,alternateTumorID from entity where parentId=='syn5522788'")
colnames(synq)<-c('patientId','alternateTumorId','fileName','tissueId','synapseId')
# synq=synq[-grep('hard-filtered',synq$fileName),]
return(synq)
}