comparison cluster.tools/select.k.from.consensus.cluster.R @ 2:b442996b66ae draft

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2:b442996b66ae

#!/usr/bin/env Rscript
# Consensus Clustering Script by Peter Waltman
# June 1, 2012
# License under Creative Commons Attribution 3.0 Unported (CC BY 3.0)
#
##usage, options and doc goes here
argspec <- c("select.k.from.consensus.clust4er.R takes a clustering from ConsensusClusterPlus
and clinical survival data and determines the right k to use.

        Usage: 
                select.k.from.consensus.cluster.R -r <results_file>
        Optional:
                -o output.png # default is stdout
                -c change.min
                -m metric (must be either:
                                        rel.change,
                                        angle,
                                        silhouette (must specify data matrix)
                                        survival (must specify survival data; uses minimal cummulative log-rank p-value)
                -d data ## for calculating silhouette plots (plotted, but not used unless specified)
                -s survival.data.fname (plotted, but not used unless specified)
                -e survival.comp (can be either all, one or both - see the mode param for gen.survival.curves for explanation)
                -z survival analysis script to be called (defaults to galaxy.gen.survival.curves.R)
\n\n")
args <- commandArgs(TRUE)
if ( length( args ) == 1 && args =="--help") { 
  write( argspec, stderr() )
  q();
}

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

spec <- matrix( c( "results.file",        "r", 1, "character",
                   "change.min",          "c", 2, "double",
                   "metric",              "m", 2, "character",
                   "survival.data",       "s", 2, "character",
                   "survival.comp",       "e", 2, "character",
                   "survival.script",     "z", 2, "character",
                   "output.format",       "f", 2, "character",
                   "cluster.class.out",   "o", 2, "character",
                   "output.report.dir",   "p", 2, "character",
                   "output.report.html",  "h", 2, "character"
                  ),
                nc=4,
                byrow=T
               )
opt <- getopt( spec=spec )

## default params for non-required params
if ( is.null( opt$output.report.dir ) ) { opt$output.report.dir <- "report" }
if ( is.null( opt$output.report.html ) ) { opt$output.report.html <- "report/index.html" }

if ( is.null( opt$change.min ) ) { opt$change.min <- 0.075 }
if ( is.null( opt$metric ) ) { opt$metric <- "difference" } ## alternate is angle }
if ( is.null( opt$survival.comp ) ) { opt$survival.comp <- "all" } ## alternate is one or both }
if ( is.null( opt$survival.script ) ) { opt$survival.script <- "galaxy.gen.survival.curves.R" } ## alternate is one or both }
if ( is.null( opt$cluster.class.out) ) { opt$cluster.class.out <- "select.cls.rda" }

if ( !file.exists( opt$output.report.dir ) ){
    dir.create(opt$output.report.dir)
}

if ( ! opt$metric %in% c( "difference", "angle", "silhouette", "survival" ) ) {
  stop( "invalid metric specified ", opt$metric, "\n" )
}

                               
opt$change.min <- as.numeric( opt$change.min )
if ( abs( opt$change.min ) > 1 ) {
  stop( "invalid angle specified:", opt$change.min, "Please specify angle in rangel [-1,0]\n" )
}
if ( opt$metric=="angle" && opt$change.min > 0 ) {
  opt$change.min <- -opt$change.min
  cat( "Using", opt$change.min, "for minimum angle\n" )
}

if ( opt$metric == "survival" &&
     ( is.null( opt$survival.data ) ||
       (! file.exists( opt$survival.data ) ) )
    ) {
  stop( "Must provide valid survival file in order to use survival as metric\n" )
}


## From the ConsensusClusterPlust package - modified by phw
CDF <- function( ml,
                 breaks=1000,
                 plot.it=TRUE ){
  if ( class(ml[[1]])=="matrix" && ( names( ml[1] ) =="2" ) ) {
    ml <- c( c(0), ml )
  }
  ##plot CDF distribution
  if ( plot.it ) {
    plot( c(0),
          xlim=c(0,1),
          ylim=c(0,1),
          col="white",
          bg="white",
          xlab="consensus index",
          ylab="CDF",
          main="consensus CDF",
          las=2 )
  }
  
  k=length(ml)
  this_colors <- rainbow(k-1)
  areaK <- c()
  for (i in 2:length( ml ) ) {
    v <- ml[[i]]
    v <- v[ lower.tri(v) ]

    #empirical CDF distribution. default number of breaks is 100    
    h = hist(v, plot=FALSE, breaks=seq(0,1,by=1/breaks))
    h$counts = cumsum(h$counts)/sum(h$counts)

    #calculate area under CDF curve, by histogram method.
    thisArea=0
    for (bi in 1:(length(h$breaks)-1)){
       thisArea = thisArea + h$counts[bi]*(h$breaks[bi+1]-h$breaks[bi]) #increment by height by width
    }
    areaK = c(areaK,thisArea)
    if ( plot.it ) lines(h$mids,h$counts,col=this_colors[i-1],lwd=2,type='l')
  }
  if ( plot.it ) legend(0.8,0.5,legend=paste(rep("",k-1),seq(2,k,by=1),sep=""),fill=this_colors)

  #Calc area under CDF change.
  deltaK=areaK[1] #initial auc at k=2
  for(i in 2:(length(areaK))){
    #proportional increase relative to prior K.
    deltaK = c(deltaK,( areaK[i] - areaK[i-1])/areaK[i-1])
  }
  return ( list( areaK=areaK, deltaK=deltaK ) )
}


load( opt$results.file )
save.image( '/home/waltman/work.local/tmp/select.dbg.rda' )

if ( opt$metric == "silhouette" ) {
  if ( ! exists( 'data' ) && ( class( data ) != "matrix" ) ) {
    stop( "Must provide valid data matrix in order to use silhouette as metric\n" )
  }
}
cons.matrices <- lapply( results[ 2:length(results) ], '[[', 'consensusMatrix' )
cls <- lapply( results[ 2:length(results) ], function( res ) return( res$consensusClass[ res$consensusTree$order ] ) )  ##'[[', 'consensusClass' )
names( cons.matrices ) <- names( cls ) <- 2:length( results )

png.fname <- file.path( opt$output.report.dir, "consensus.sel.criteria.CDF.png")
plot.dev <- png( png.fname,
                 width=11,
                 height=8.5,
                 units='in',
                 res=72 )
##  this will calculate the CDF, plus plot them
rel.delta <- CDF( cons.matrices, breaks=1000, plot.it=TRUE )$deltaK
dev.off()
names( rel.delta ) <- seq( from=2, by=1, length=length( rel.delta ) )
vector.of.metric.changes <- rel.delta

main.txt <- ", per Size K"
ylab.txt <- ""

main.txt <- paste( "Relative Change in Area", main.txt, sep="" )
ylab.txt <- paste( "relative change in area under CDF curve", ylab.txt, sep="" )
png.fname <- file.path( opt$output.report.dir, "consensus.sel.criteria.diff.png")

plot.dev <- png( png.fname,
                 width=11,
                 height=8.5,
                 units='in',
                 res=72 )
plot( as.numeric( names( vector.of.metric.changes ) ),
      vector.of.metric.changes,
      main=main.txt,
      ylab=ylab.txt,
      xlab="Cluster size (K)",
      type='b' )
dev.off()

k.select <- vector.of.metric.changes[ vector.of.metric.changes < opt$change.min ]
if ( length( k.select ) > 1 ) {
  k.select <- k.select[1]
} else {
  if ( length( k.select ) == 0 ) {
    k.select <- vector.of.metric.changes[ length( vector.of.metric.changes ) ]
  } else {
    ## do nothing
  }
}
k.select <- as.numeric( names( k.select ) )
## find the search range
k.search.range <- (k.select-2):(k.select+2)
k.search.range <- k.search.range[ k.search.range %in% as.numeric( names( vector.of.metric.changes ) ) ]
k.search.range <- vector.of.metric.changes[ as.character( k.search.range ) ]
k.search.range <- k.search.range[ k.search.range  < 0.25 ]
k.search.range <- k.search.range[ k.search.range > 0.025 ]
k.search.range <- names( k.search.range )


if ( exists("data") ) {
  ## what direction is the clustering in? rows or cols?
  elts <- unique( names( results[[2]]$consensusClass ) )
  if ( all( elts %in% colnames( data ) ) ) {
    ## sample clusters
    data.dist <- dist( t( data ) )
    cls <- lapply( cls, function( x ) return( x[ colnames( data ) ] ) )
  } else if ( all( elts  %in% rownames( data ) ) ) {
    data.dist <- dist( data )
    cls <- lapply( cls, function( x ) return( x[ rownames( data ) ] ) )
  } else {
    stop( "incompatible cluster results and data matrix\n" )
  }

  
  sils <- lapply( cls,
                  silhouette,
                  dist=data.dist )
  sils <- sapply( sils,
                  function(x) {
                    return( summary( x )$avg.width )
                  }
                 )

  png.fname <- file.path( opt$output.report.dir, "consensus.sel.silhouette.png")

  plot.dev <- png( png.fname,
                   width=11,
                   height=8.5,
                   units='in',
                   res=72 )
  plot( as.numeric( names( sils ) ),
        sils,
        main="Average Silhouette Widths, per Cluster Size K",
        ylab="average silhouette width (correlation distance)",
        xlab="Cluster size (K)",
        type='b' )
  dev.off()

  ## if the metric is silhouette, use that (but only over the k's that are on the rel-change "elbow"
  if ( opt$metric == "silhouette" ) {
    names( sils ) <- names( cls )

    sils <- sils[ k.search.range ]
    k.select <- as.numeric( names( sils[ sils == max( sils, na.rm=T ) ] ) )
  }
}

if ( ! is.null( opt$survival.data ) ) {
  if ( ! file.exists( opt$survival.data ) ) {
    stop( 'specified clinical/survival file can not be found:', opt$survival.data, "\n" )
  }

  if ( opt$metric == "survival" ) {
    pvals <- numeric()

    for ( cl in cls ) {
      cons.class.file <- "tmp.class.tab"
      tmp.cl <- cbind( gsub( "\\.", "-", names( cl ) ), as.integer(cl) )
      colnames( tmp.cl ) <- c( "ID", "class" )
      write.table( tmp.cl, cons.class.file, sep="\t", row.names=FALSE, quote=FALSE )
      
      cmd.string <- opt$survival.script

      ## get the consensusClass file that's associated with the k.select
      cmd.string <- paste( cmd.string, "-c", cons.class.file )
      cmd.string <- paste( cmd.string, "-s", opt$survival.data )
      cmd.string <- paste( cmd.string, "-m", opt$survival.comp )
      cmd.string <- paste( cmd.string, "-p" )
      pvals <- c( pvals, as.numeric( system( cmd.string, intern=T ) ) )
    }
    names( pvals ) <- names( cls )
    

    png.fname <- file.path( opt$output.report.dir, "consensus.sel.criteria.survival.png" )

    plot.dev <- png( png.fname,
                     width=11,
                     height=8.5,
                     units='in',
                     res=72 )
    plot( as.numeric( names( pvals ) ),
          -log( pvals ),
          main="Average Log-rank p-values (-log), per Cluster Size K",
          ylab="Average log-rank p-values (-log)",
          xlab="Cluster size (K)",
          type='b' )
    dev.off()
    

    pvals <- pvals[ ksearch.range ]
    k.select <- as.numeric( names( pvals[ pvals == min( pvals, na.rm=T ) ] ) )
  }
  
  cmd.string <- opt$survival.script

  ## get the consensusClass file that's associated with the k.select 
  cl <- cls[[ as.character( k.select ) ]] 
  cl <- cbind( names( cl ), as.integer(cl) )
  colnames( cl ) <- c( "ID", "class" )
  write.table( cl, opt$cluster.class.out, sep="\t", row.names=FALSE, quote=FALSE )

  cmd.string <- paste( cmd.string, "-c", opt$cluster.class.out )
  cmd.string <- paste( cmd.string, "-s", opt$survival.data )
  cmd.string <- paste( cmd.string, "-m", opt$survival.comp )
  
  survival.out.file <- paste( opt$output.report.dir, "survival.png", sep="/" )
  cmd.string <- paste( cmd.string, "-o", survival.out.file )
  output <- system( cmd.string, intern=T )
  cat( output, sep="\n" )
} else {
  ## get the consensusClass file that's associated with the k.select 
  cl <- cls[[ as.character( k.select ) ]] 
  cl <- cbind( names( cl ), as.integer(cl) )
  colnames( cl ) <- c( "ID", "class" )
  write.table( cl, opt$cluster.class.out, sep="\t", row.names=FALSE, quote=FALSE )
}

treecl.res <- results[[ k.select ]]$consensusTree
## cl should already exist, but re-create it just in case
cl <- cls[[ as.character( k.select ) ]] 


select.result <- results[[ k.select ]]
## over-write the tabular version of the opt$cluster.class.out with an RData file
save( file=opt$cluster.class.out, treecl.res, cl, select.result, data ) 

report_str = paste( "k selected by consensus clustering and user-specified metric, ", opt$metric, ", is ", k.select, "\n", sep="" )

pngs = list.files(path=opt$output.report.dir, patt="png")
html.out <- paste( "<html>", report_str,
                   paste( paste( paste( "<div><img src=\'", pngs, sep="" ), "\'/></div>", sep="" ), collapse=""),
                   "</html>" )
cat( html.out, file=opt$output.report.html )