@@ -28,7 +28,7 @@

 ##

 ##################################################################

-normalize.quantiles <- function(x,copy=TRUE){

+normalize.quantiles <- function(x,copy=TRUE,keep.names=FALSE){

   rows <- dim(x)[1]

   cols <- dim(x)[2]

@@ -45,11 +45,16 @@ normalize.quantiles <- function(x,copy=TRUE){

   #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");

+  mat <- .Call("R_qnorm_c_handleNA",x,copy, PACKAGE="preprocessCore");

+  if(keep.names){

+    rownames(mat) <- rownames(x)

+    colnames(mat) <- colnames(x)

+  }

+  mat

 }

-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){

+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,keep.names=FALSE){

   calc.var.ratios <- function(x){

     cols <- dim(x)[2]

@@ -104,5 +109,9 @@ normalize.quantiles.robust <- function(x,copy=TRUE,weights=NULL,remove.extreme=c

     }

   }

-  .Call("R_qnorm_robust_c",x,copy,weights,as.integer(use.median),as.integer(use.log2),as.integer(use.huber),PACKAGE="preprocessCore")

+  mat <- .Call("R_qnorm_robust_c",x,copy,weights,as.integer(use.median),as.integer(use.log2),as.integer(use.huber),PACKAGE="preprocessCore")

+  if(keep.names){

+    rownames(mat) <- rownames(x)

+    colnames(mat) <- colnames(x)

+  }

 }

@@ -14,6 +14,8 @@

   \item{copy}{Make a copy of matrix before normalizing. Usually safer to

     work with a copy, but in certain situations not making a copy of the

     matrix, but instead normalizing it in place will be more memory friendly.}

+  \item{keep.names}{Boolean option to preserve matrix row and column names in

+    output.}

 }

 \details{This method is based upon the concept of a quantile-quantile

   plot extended to n dimensions. No special allowances are made for

@@ -23,6 +23,8 @@

     chip, otherwise uses a weighted mean}

   \item{use.log2}{work on log2 scale. This means we will be using the

     geometric mean rather than ordinary mean}

+  \item{keep.names}{Boolean option to preserve matrix row and column names in

+    output.}

 }

 \details{This method is based upon the concept of a quantile-quantile 

   plot extended to n dimensions. Note that the matrix is of intensities

@@ -51,4 +51,13 @@ if (all(abs(normalize.quantiles.use.target(y,y.norm.target.truth) - y.norm.truth

 }

-

+x <- matrix(c(100,15,200,250,110,16.5,220,275,120,18,240,300),ncol=3)

+rownames(x) <- letters[1:4]

+colnames(x) <- LETTERS[1:3]

+y <- normalize.quantiles(x, keep.names = TRUE)

+if(!all(colnames(x)==colnames(y))){

+    stop("Disagreement between initial and final column names despite keep.names=TRUE")

+}

+if(!all(rownames(x)==rownames(y))){

+    stop("Disagreement between initial and final row names despite keep.names=TRUE")

+}