# # original author: Tobias Meissner
# library(DESeq2)
# library(RColorBrewer)
# library(gplots)
# library(edgeR)
# library(dplyr)
# library(SPIA)
# library(graphite)
# library(org.Hs.eg.db)
# library(ggplot2)
# library(corrplot)
# library(EDASeq)
#
# source('src/func.R')
#
# ################################################################################
# # get command line arguments
# ################################################################################
# args <- commandArgs(trailingOnly = TRUE)
#
# patientData <- args[1] #'~/AWS/storage/pats/rna/CCD018/CCD018.ReadsPerGene.out.tab'
# tumorType <- args[2] #'BRCA'
# libType <- args[3] #'stranded-reverse' # options: unstranded, stranded, stranded-reverse
# refPath <- args[4] #'~/AWS/s3/averaprojects/CRC'
# outDir <- args[5] #'~/Downloads/temp/'
#
# if(dir.exists(outDir)) {
# print('Output directory exists')
# } else {
# dir.create(outDir)
# }
#
# ################################################################################
# # load the reference cohort
# ################################################################################
# source('src/loadRef.R')
# allTumorTypes <- c('BRCA', 'OV', 'LAML',
# 'FALLOPIAN')
# if (tumorType %in% allTumorTypes) {
# loadRef(tumorType, refPath)
# } else {
# print('Tumor Type is not supported')
# stop()
# }
#
# vsdRefMat <- assay(reference$reference_vsd)
#
# ################################################################################
# # read in patient data
# ################################################################################
# source('src/patientDataIn.R')
# df <- patData(patientData)
#
# ################################################################################
# # diff expression against healthy controls and/or tcga matched normal
# ################################################################################
# source('src/diff.R')
# diffExpr <- diff(tumorType, vsdRefMat, df)
#
# res1x <- as.data.frame(diffExpr$res1)
# res1x <- cbind(id=rownames(res1x), res1x) #,@call=patientREC$Pcalls)
# res1Sig <- arrange(res1x[which(res1x$padj < 0.05), ], padj)
# write.table(res1Sig, paste0(outDir, 'patient_vs_normal.csv'), sep='\t', row.names = FALSE)
#
# if(any(reference$group=='MNORMAL')) {
# res2x <- as.data.frame(diffExpr$res2)
# res2x <- cbind(id=rownames(res2x), res2x) #,@call=patientREC$Pcalls)
# res2Sig <- arrange(res2x[which(res2x$padj < 0.05), ], padj)
# write.table(res2Sig, paste0(outDir, 'patient_vs_mnormal.csv'), sep='\t', row.names = FALSE)
# }
#
# ################################################################################
# # viz
# ################################################################################
# source('src/viz.R')
# palette(rainbow(4))
#
# if(any(reference$group=='MNORMAL')) {
# pdf(paste0(outDir, 'qc.pdf'))
# # add sample to reference
# sRef <- samleToRef(df,
# reference,
# loggeomeansRef,
# vsdRefMat,
# diffExpr$selNormal,
# diffExpr$selMNormal,
# diffExpr$selTumor
# )
# } else {
# pdf(paste0(outDir, 'qc.pdf'))
# # add sample to reference
# sRef <- samleToRef(df,
# reference,
# loggeomeansRef,
# vsdRefMat,
# diffExpr$selNormal,
# NULL,
# diffExpr$selTumor
# )
# }
#
# ## library size
# my.libplot(diffExpr$dds, col=factor(sRef$des), legend=sRef$des)
#
# ## transformed data, boxplot
# my.vsdplot(sRef$vsdMat, col=factor(sRef$des), sRef$des)
#
# ## relative log expression
# plotRLE(sRef$vsdMat,
# col=factor(sRef$des),
# outline=FALSE,
# las=3,
# ylim=c(-.2, .2),
# ylab="Relative Log Expression",
# cex.axis=1,
# cex.lab=1,
# names=sRef$des
# )
#
# ## pca
# my.pca(sRef$vsdMat, sRef$des)
#
# ## sample-to-sample dist
# my.ssDist(sRef$vsdMat, sRef$des)
# dev.off()
#
#
#
#
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