inst/breast.R

In doppelgangR: Identify likely duplicate samples from genomic or meta-data

library(BiocParallel)

breast.packages <- c("breastCancerMAINZ", "breastCancerNKI", "breastCancerTRANSBIG", "breastCancerUNT", "breastCancerUPP", "breastCancerVDX")

other.packages <- "WGCNA"

if (!require(BiocManager))
    stop("You need to install BiocManager from CRAN")


for (pkg in breast.packages){
    if(!require(package=pkg, character.only=TRUE)){
        print(paste("Need to install", pkg))
        install(pkg, update=FALSE)
    }
}


for (pkg in other.packages){
    if(!require(package=pkg, character.only=TRUE)){
        print(paste("Need to install", pkg))
        install(pkg, update=FALSE)
    }
}

if(file.exists("breast_esets.rda")) {
  load("breast_esets.rda")
}else{
  esets <- bplapply(breast.packages, function(pkg) {
    print(pkg)
    library(Biobase)
    esetname <- tolower(sub("breastCancer", "", pkg))
    data(list = esetname)
    output <- get(esetname)
    output <- output[!is.na(featureData(output)$EntrezGene.ID),]
    merge.probeset <- WGCNA::collapseRows(
      datET = exprs(output),
      rowGroup = featureData(output)$EntrezGene.ID,
      rowID = featureNames(output)
    )
    output <- output[merge.probeset$selectedRow,]
    featureNames(output) <- featureData(output)$EntrezGene.ID
    return(output)
  })
  names(esets) <- sub("breastCancer", "", breast.packages)
  save(esets, file = "breast_esets.rda", compress = "bzip2")
}

library(Biobase)
table(table(unlist(lapply(esets, sampleNames))))

#set.seed(1)
#eset2 <- lapply(esets, function(x) x[sample(1:nrow(x), 300), ])

library(doppelgangR)

if(file.exists("breast_dop.rda")){
  load("breast_dop.rda")
}else{
  dop <- doppelgangR(esets, phenoFinder.args=NULL, smokingGunFinder.args=NULL,
                     outlierFinder.expr.args=list(bonf.prob = 1.0, transFun = atanh, tail = "upper"))
  warnings()
  save(dop, file="breast_dop.rda")
}
##Look for smoking guns only:
for (i in 1:length(esets))
    esets[[i]]$samplenames <- sampleNames(esets[[i]])
dop.gun <- doppelgangR(esets, manual.smokingguns=c("alt_sample_name", "unique_patient_ID", "samplenames"),
                       phenoFinder.args=NULL, corFinder.args=NULL, impute.knn.args=NULL)
save(dop.gun, file="breast_dopgun.rda")
write.csv(summary(dop.gun), file="breast_dopgun.csv")

write.csv(dop@summaryresults, file="breast_dop.csv")

pdf("breast_dop.pdf")
plot(dop)
dev.off()

library(Biobase)
esets2 <- esets[c("UNT", "UPP")]
esets2$UPP = esets2$UPP[, which(esets2$UPP$er==1)]
unt.dops <- grep("^KIU", sub("UNT:", "", summary(dop)[, 1]), val=TRUE)
keep = sort(na.omit(unique(c(which(sampleNames(esets2$UNT) %in% unt.dops), which(esets2$UNT$er==0)))))
esets2$UNT <- esets2$UNT[, keep]
doper <- doppelgangR(esets2, phenoFinder.args=NULL, smokingGunFinder.args=NULL,
                     outlierFinder.expr.args=list(bonf.prob = 1.0, transFun = atanh, tail = "upper"))

doper.ids <- summary(doper)[, 1:2]
doper.ids <- paste(doper.ids[, 1], doper.ids[, 2])

dop.ids <- summary(dop)[, 1:2]
dop.ids <- paste(dop.ids[, 1], dop.ids[, 2])
dop.ids <- grep("^UNT", dop.ids, val=TRUE)
lapply(esets, function(eset) table(eset$er))
lapply(esets2, function(eset) table(eset$er))  #UNT is 32/72 ER+, UPP is all 213 ER+
summary(dop.ids %in% doper.ids)  #all 32 ER+ doppelgangers are found, only the 5 ER- doppelgangers that were removed are gone.