##################################################################
 ##
 ## file: normalize.quantiles.R
 ##
 ## For a description of quantile normalization method see
 ##
 ##  Bolstad, B. M., Irizarry R. A., Astrand, M, and Speed, T. P. (2003)(2003)
 ##  A Comparison of Normalization Methods for High
 ##  Density Oligonucleotide Array Data Based on Bias and Variance.
 ##  Bioinformatics 19,2,pp 185-193
 ##
 ## History
 ## Pre Aug 23, 2003 Two years worth of stuff
 ## Aug 23, 2003 - Added use.log2 to "robust",
 ##                added ability to pass additional parameters
 ##                to normalize.AffyBatch.Quantiles.robust
 ##                changed pmonly parameters on functions
 ##                so that it is now a string argument "type"
 ##                the options are pmonly, mmonly, together, separate
 ## Jan 31, 2004 - put a check for an integer matrix and force coercision to
 ##                doubles if required in normalize.quantiles
 ## Mar 13, 2005 - Modifications to normalize.quantiles.robust including removing
 ##                approx.method which never got implemented. Making it a use a .Call()
 ##                rather than a .C()
 ##
 ## Sep 20, 2006 - fix .Call in normalize.quantiles.robust
 ## May 20, 2007 - port to preprocessCore. Remove anything to do with AffyBatch Objects
 ##
 ##################################################################
 
 normalize.quantiles <- function(x,copy=TRUE){
 
   rows <- dim(x)[1]
   cols <- dim(x)[2]
 
   if (!is.matrix(x)){
     stop("Matrix expected in normalize.quantiles")
   }
 
   if (is.integer(x)){
     x <- matrix(as.double(x),rows,cols)
     copy <- FALSE
   }
 
   #matrix(.C("qnorm_c", as.double(as.vector(x)), as.integer(rows), as.integer(cols))[[1]], rows, cols)
 

 ##  .Call("R_qnorm_c",x,copy, PACKAGE="preprocessCore");
   .Call("R_qnorm_c_handleNA",x,copy, PACKAGE="preprocessCore");

 }
 
 
 normalize.quantiles.robust <- function(x,copy=TRUE,weights=NULL,remove.extreme=c("variance","mean","both","none"),n.remove=1,use.median=FALSE,use.log2=FALSE){
 
   calc.var.ratios <- function(x){
     cols <- dim(x)[2]
     vars <- apply(x,2,var)
     results <- matrix(0,cols,cols)
     for (i in 1:cols-1)
       for (j in (i+1):cols){
         results[i,j] <- vars[i]/vars[j]
         results[j,i] <- vars[j]/vars[i]
       }
     results
   }
 
   calc.mean.dists <- function(x){
     cols <- dim(x)[2]
     means <- colMeans(x)
     results <- matrix(0,cols,cols)
     for (i in 1:cols-1)
       for (j in (i+1):cols){
         results[i,j] <- means[i] - means[j]
         results[j,i] <- means[j] - means[i]
       }
     results
   }
 
   use.huber <- FALSE
   remove.extreme <- match.arg(remove.extreme)
 
   rows <- dim(x)[1]
   cols <- dim(x)[2]
 
   if (is.null(weights)){
     weights <- .Call("R_qnorm_robust_weights",x,remove.extreme,as.integer(n.remove),PACKAGE="preprocessCore")
   } else {
     if (is.numeric(weights)){
       if (length(weights) != cols){
         stop("Weights vector incorrect length\n")
       }
       if (sum(weights > 0) < 1){
         stop("Need at least one non negative weights\n")
       }
       if (any(weights < 0)){
         stop("Can't have negative weights")
       }
     } else {
       if (weights =="huber"){
         use.huber <- TRUE
         weights <- rep(1,cols)
       } else {
         stop("Don't recognise weights argument as valid.")
       }
     }
   }
   
   .Call("R_qnorm_robust_c",x,copy,weights,as.integer(use.median),as.integer(use.log2),as.integer(use.huber),PACKAGE="preprocessCore")
 }