comparison cluster.tools/format.raw.TCGA.RNASeq.data.R @ 1:dddfeedb85af draft

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1:dddfeedb85af

#!/usr/bin/env Rscript
## 
## formats raw clinical data from TCGA to contain a single status & time colums
##
## Input (required):
##    - clinical data
## Input (optional):
##    - status & time columns: (NOT USED IN THIS SCRIPT - see comment below)
##         ideally, a better design would allow a user to specify 1 or more columns
##         to check for the status & time columns - however, due to the necessities
##         required to pre-process the TCGA clinical data, the script would not be
##         generalizeable - and for this reason, the TCGA columns are hard-coded.
##
## Output: a re-formatted clinical file containing 3 columns: sample-ID, status & time
##
## Date: August 21, 2012
## Author: Peter Waltman
##

##usage, options and doc goes here
argspec <- c("format.raw.TCGA.clinical.data.R takes a clustering from ConsensusClusterPlus and clinical survival data
and generates a KM-plot, along with the log-rank p-values

        Usage: 
                format.raw.TCGA.clinical.data.R -c <clinical.file> 
        Options:
                -o <output file> (tab-delimited (3 col: sample_id <tab> status <tab> time))
              ")
args <- commandArgs(TRUE)
if ( length( args ) == 1 && args =="--help") { 
  write(argspec, stderr())
  q();
}

## some helper fn's
write.2.tab <- function( mat,
                         fname ) {
  mat <- rbind( colnames( mat ), mat )
  mat <- cbind( c( "ID", rownames( mat )[-1] ),
                      mat )
  write.table( mat, fname, sep="\t", row.names=FALSE, col.names=FALSE, quote=FALSE )
}

fix.genes <- function( mat ) {
  ## filter out the unknowns
  mat <- mat[ ( !  grepl( "^\\?", rownames( mat ) ) ), ]  

  genes <-  rownames( mat )
  ## select the HUGO name portion of the "gene-names" that are in the TCGA matrices
  genes <- sapply( strsplit( genes, "\\|" ), function(x) x[1] )

  if ( any( duplicated( genes ) ) ) {
    dupes <- unique( genes[ duplicated( genes ) ] )
    no.duped.mat <- which( ! genes %in% dupes )
    no.duped.mat <- mat[ no.duped.mat, , drop=FALSE ]

    ## now merge in the duplicates
    for ( dup in dupes ) {
      duped.mat <- mat[ grepl( paste( "^", dup, sep="" ), rownames( mat ) ), ]
      duped.mat <- apply( duped.mat, 2, mean, na.rm=TRUE )
      no.duped.mat <- rbind( no.duped.mat, duped.mat )
      ## weird bit of follow up to make sure the new row/gene is named correctly
      rownames( no.duped.mat ) <- c( rownames( no.duped.mat )[ -nrow(no.duped.mat) ],
                                     paste( dup, "|0000", sep="" ) )
    }
    mat <- no.duped.mat; rm( no.duped.mat ); gc()
  }

  rownames( mat ) <- sapply( strsplit( rownames( mat ), "\\|" ), function(x) x[1] )
  return( mat )
}

fix.sample.ids <- function( mat ) {
  colnames( mat ) <- gsub( "\\.", "-", colnames( mat ) )
  return( mat )
}

lib.load.quiet <- function( package ) {
   package <- as.character(substitute(package))
   suppressPackageStartupMessages( do.call( "library", list( package=package ) ) )
}
lib.load.quiet(getopt)

spec <- matrix( c( "dataset",             "d", 1, "character",
                   "log.tranform",        "l", 2, "character",
                   "filter.low.variant",  "f", 2, "integer",
                   "output.fname",        "o", 2, "character"
                  ),
                ncol=4,
                byrow=TRUE
               )
opt <- getopt( spec=spec )

##set some reasonable defaults for the options that are needed,
##but were not specified.
if ( is.null(opt$output.fname ) ) { opt$output.fname <-file.path( getwd(), "formated.TCGA.RNASeq.data" ) }
if ( is.null(opt$log.transform ) ) {
  opt$log.transform <- TRUE
} else {
  opt$log.transform <- ( tolower( opt$log.transform ) %in% "yes" )
}

if ( is.null(opt$filter.low.variant ) ) { opt$log.transform <- 10 } 


data <- as.matrix( read.delim( opt$dataset, row.names=1, check.names=F ) )
data <- fix.genes( data )
data <- fix.sample.ids( data )

if ( opt$filter.low.variant > 0 ) {
  rpkm.meds <- apply( data, 1, median, na.rm=TRUE )
  rpkm.meds <- rpkm.meds >= opt$filter.low.variant
  data <- data[ rpkm.meds, ]
}

if ( opt$log.transform ) {
  data <- log( data + 1 )
}

write.2.tab( data, opt$output.fname )