comparison cluster.tools/rnaseq.feature.selection.R @ 1:dddfeedb85af draft

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1:dddfeedb85af

#!/usr/bin/env Rscript
## RNASeq selection script by Peter Waltman
## August 21, 2011
## License under Creative Commons Attribution 3.0 Unported (CC BY 3.0)
##
#usage, options and doc goes here  - NEEDS TO BE UPDATED  ---- TBD
argspec <- c("ipl.feature.selection.R takes a set of results from Paradigm, and filters for features that are
active, inactive or modulated above a given IPL threshold over a sufficient percentage of samples.

        Usage: 
                ipl.feature.selection.R -d <data.file> 
        Optional:
                -o <output.name>
                -g <genes-only>   ## to set if only returning genes (default is all features)
                -f <filter.type>       ## filter.type must be either 'modulated', 'active'or 'inactive' (default is modulated)
                -t <threshold>    ## the threshold to use for the filter (default is 0.25)
                -p <perc.pass>    ## the percentage of samples that must pass the filter (default is 0.33)
                -v <verbose>      ## to set verbose on

                \n\n")
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 )
}

lib.load.quiet <- function( package ) {
   package <- as.character(substitute(package))
   suppressPackageStartupMessages( do.call( "library", list( package=package ) ) )
}
## MAXIMUM absolute deviation function
maxad <- function( x, na.rm=FALSE, center=median(x, na.rm=na.rm ) ) {
  return( max( abs(x), na.rm=na.rm ) - center )
}

lib.load.quiet(getopt)

spec <- matrix( c( "data.fname",         "d", 1, "character",
                   "output.fname",       "o", 2, "character",
                   "var.method",         "m", 2, "character", ## must be either 'active', 'inactive' or 'modulated'
                   "z.transform",        "z", 2, "character",
                   "perc.pass",          "p", 2, "numeric"
                   ),
                nc=4,
                byrow=TRUE
               )

opt <- getopt( spec=spec )
data <- as.matrix( read.delim( opt$data.fname, header=T, row.names=1 , check.names=FALSE ) )
#set some reasonable defaults for the options that are needed,
#but were not specified.
if ( is.null( opt$perc.pass ) ) { opt$perc.pass=1500 }
if ( opt$perc.pass < 0  ) {
  stop( "please specify a positive number for the percentage of samples that pass the filter (if applicable)" )
} else {
  if ( opt$perc.pass < 1  ) {
    opt$perc.pass <- floor( opt$perc.pass * nrow( data ) )
  }
}
      
if ( is.null( opt$var.method ) ) { opt$var.method <- 'mad' }
if ( is.null( opt$z.transform ) ) {
  opt$z.transform <- TRUE
} else {
  opt$z.transform <- ( tolower( opt$z.transform ) %in% "yes" )
}
if ( is.null( opt$output.fname ) ) opt$output.fname <- "high.var.rnaseq.tab"

if ( opt$z.transform ) {
  ##normalize it to have mean==0, std==1
  data <- t( data )
  data <- t( scale( data ) )
}


my.var.fn <- get( opt$var.method )
my.var.vals <- apply( data, 1, my.var.fn, na.rm=T )
filter.vect <- sort( my.var.vals, dec=TRUE, index=TRUE )$ix[ 1:opt$perc.pass ]

data <- data[ filter.vect, ]
write.2.tab( data, opt$output.fname )