MIS-Kmeans:

##' This function provide a useful function to visualize gene expression matrix.
##' ratio adjusted gene-wise normalization will be utilized.
##' Cluster assignment and non-zero weight genes must be provided.
##'
##' nothing at this moment
##' @title A function for visualization gene expression matrix
##' @param astudy p by n matrix.
##' p represents number of features and n represents number of samples
##' @param aCs cluster assignment labels. Total length should be n.
##' @param ws Logical vector indicating whether some genes should show up in the heatmap.
##' Length of ws should be p
##' @param Rowv If TRUE, genes will be clustered using hierarchical clustering algorithm.
##' If NA, gene will use the order from argument geneOrder.
##' @param geneOrder If Rowv is NA, then the genes will be order by argument of geneOrder.
##' Length of geneOrder should be the number of non-zero weight.
##' @param \dots Other parameters inherited from function heatmap function.
##' @return Return heatmap object. Refer to ?heatmap
##' @author Zhiguang Huo
##' @export
##' @examples
##' ######################################
##' ## generate data
##' set.seed(15213)
##'
##' G = 1000
##' n11 = 100
##' n12 = 100
##' n13 = 150
##' label1 = c(rep(1,n11),rep(2,n12),rep(3,n13))
##'
##' P0 = 0.6
##' P1 = 0.1
##' P2 = 0.1
##' P3 = 0.1
##' P4 = 0.1
##' sd = 0.5
##'
##' G0 = G*P0  # nonDE genes
##' G1 = G*P1  # DE H-L
##' G2 = G*P2	# DE L-H
##' G3 = G*P3
##' G4 = G*P4
##'
##'
##' mu111 = runif(G1,-0.25,0.25)
##' mu112 = runif(G1,0.5,1)
##' mu113 = runif(G1,-1,-0.5)
##'
##' mu121 = runif(G2,-1,-0.5)
##' mu122 = runif(G2,-0.25,0.25)
##' mu123 = runif(G2,0.5,1)
##'
##' mu131 = runif(G3,-1,-0.5)
##' mu132 = runif(G3,-0.25,0.25)
##' mu133 = runif(G3,0.5,1)
##'
##' mu14 = runif(G4,-0.25,0.25)
##' mu10 = runif(G0,-0.25,0.25)
##'
##' Data111 = matrix(rnorm(n11*G1,mu111,sd^2),nrow=G1)
##' Data112 = matrix(rnorm(n12*G1,mu112,sd^2),nrow=G1)
##' Data113 = matrix(rnorm(n13*G1,mu113,sd^2),nrow=G1)
##' Data11 = cbind(Data111,Data112,Data113)
##'
##' Data121 = matrix(rnorm(n11*G2,mu121,sd^2),nrow=G2)
##' Data122 = matrix(rnorm(n12*G2,mu122,sd^2),nrow=G2)
##' Data123 = matrix(rnorm(n13*G2,mu123,sd^2),nrow=G2)
##' Data12 = cbind(Data121,Data122,Data123)
##'
##' Data131 = matrix(rnorm(n11*G3,mu131,sd^2),nrow=G3)
##' Data132 = matrix(rnorm(n12*G3,mu132,sd^2),nrow=G3)
##' Data133 = matrix(rnorm(n13*G3,mu133,sd^2),nrow=G3)
##' Data13 = cbind(Data131,Data132,Data133)
##'
##' Data14 = matrix(rnorm((n11+n12+n13)*G4,mu14,sd^2),nrow=G4)
##'
##' Data10 = matrix(rnorm((n11+n12+n13)*G0,mu10,sd^2),nrow=G0)
##'
##' S1 = rbind(Data10,Data11,Data12,Data13,Data14)
##'
##' getWsHeatmap(S1,label1,main="Visualization of S1")
##'
getWsHeatmap <- function(astudy,aCs,ws=NULL, Rowv=NA,geneOrder=NULL,maxIndex=3,...){
   if(is.null(ws))
     ws=rep(1,nrow(astudy))
   astudy = astudy[ws!=0,]
   coll <- NULL
   resHeatmap <- NULL
   label <- NULL
   uniCs = unique(aCs)
   uniorderCs = sort(uniCs)

   for(i in uniorderCs){  
     if(is.null(resHeatmap)){
       resHeatmap = astudy[,i==aCs]
       label = rep(i,sum(i==aCs))
     } 
     else {
       resHeatmap = cbind(resHeatmap,astudy[,i==aCs])
       label = c(label,rep(i,sum(i==aCs)))
     }
   }
   for(alabel in unique(label))
     coll[which(label==alabel)]=palette()[alabel]    
   finalRes = t(scale(t(resHeatmap)))
   if(!is.null(geneOrder)){
     finalRes = finalRes[geneOrder,]
   }
   outIndex = abs(finalRes)>maxIndex 
   finalRes[outIndex] = maxIndex*sign(finalRes)[outIndex]
   B=16
   return(heatmap(finalRes,Rowv=Rowv,ColSideColors=coll,
 	  col= rgb(c(rep(0, B), (0:B)/B), c((B:0)/16, rep(0, B)), rep(0, 2*B+1))
   ,scale='none',Colv=NA,...)  )
 }