R/utils.R

Defines functions pv.check1 heatmap.3 checkinvalid pv.setMask pv.checkValue pv.morethanone pv.attributePCA pv.attributematrix pv.attname pv.get_scores pv.getOverlapData pv.nums2labels pv.occ2matrix pv.removeComp pv.add_consensus pv.getoneorNA pv.get_reads pv.getPlotData pv.DataType2Peaks pv.peaks2DataType

pv.peaks2DataType <- function(peaks,datatype=DBA_DATA_DEFAULT) {
   
   res <- peaks
   
   if(is.null(peaks)) {
      return(NULL)
   }   
   
   if(datatype==DBA_DATA_FRAME) {
      return(peaks)	
   }
   
   if(class(peaks)=="data.frame") {
      if(nrow(peaks)==0) {
         return(NULL)
      }   
   }
   
   if(datatype==DBA_DATA_RANGEDDATA) {
      if(class(peaks)!="RangedData") {
         #require(IRanges)
         cnames <- colnames(peaks)
         cnames[1:3] <- c("space","start","end")
         colnames(peaks) <- cnames
      }
      res <- as(peaks,"RangedData")
   }
   
   if(datatype==DBA_DATA_GRANGES) {
      #require(GenomicRanges)
      if(class(peaks)=="RangedData") {
         res <- suppressWarnings(as(peaks,"GRanges"))
      } else if (class(peaks) != "GRanges") {
         res <- GRanges(Rle(peaks[,1]),IRanges(peaks[,2],width=peaks[,3]-peaks[,2]+1,names=rownames(peaks)),
                        strand <- Rle("*", length(seqnames)))
         if(ncol(peaks)>3) {
            mdata <- data.frame(peaks[,4:ncol(peaks)])
            colnames(mdata)  <- colnames(peaks)[4:ncol(peaks)]
            elementMetadata(res) <- mdata
         }   	
      }
   }   
   
   return(res)
   
}

pv.DataType2Peaks <- function(RDpeaks){
   
   if(is.null(RDpeaks)) {
      return(NULL)
   }
   
   if(class(RDpeaks)=='logical') {
      return(RDpeaks)
   }
   if(class(RDpeaks)=='integer') {
      return(RDpeaks)
   }
   if(class(RDpeaks)=='numeric') {
      return(RDpeaks)
   }
   if(class(RDpeaks)=='character') {
      return(RDpeaks)
   }
   if(class(RDpeaks)!="data.frame") {
      #require(IRanges)
      res <- as.data.frame(RDpeaks)[,-4]
      cnames <- colnames(res)
      cnames[1:3] <- c("CHR","START","END")
      colnames(res) <- cnames
      if(class(RDpeaks)=="GRanges") {
         res <- res[,-4]	
      }
   } else {
      res <- RDpeaks
   }
   return(res)
}

pv.getPlotData <- function(pv,attributes=PV_GROUP,contrast=1,method=DBA_DESEQ2,th=0.05,
                           bUsePval=FALSE,bNormalized=T,report,
                           bPCA=F,bLog=T,minval,maxval,mask,fold=0,
                           bFlip=FALSE) {
   
   if(contrast > length(pv$contrasts)) {
      stop('Specified contrast number is greater than number of contrasts')
      return(NULL)
   }
   
   con <- pv$contrasts[[contrast]]
   
   if(missing(report)) {
      report <- pv.DBAreport(pv,contrast=contrast,method=method,th=th,bUsePval=bUsePval,
                             bNormalized=bNormalized,bCounts=T,bSupressWarning=T,
                             minFold=fold,bFlip=bFlip)
      if(is.null(report)) {
         stop('Unable to plot -- no sites within threshold')	
      }
   } else {
      report <- report[abs(report$Fold)>=fold,]
   }
   
   if(nrow(report)==1) {
      stop("Only one site to plot -- need at least 2!")   
   }
   
   if(!missing(mask)){
      if (!is.logical(mask)) {
         if (max(mask) > length(pv$peaks)) {
            stop("Invalid sample number in mask.",call.=F)
         }
         temp <- rep(F, length(pv$peaks))
         temp[mask] <- T
         mask <- temp
      }
      if(!bFlip) {
         group1 <- con$group1 & mask
         group2 <- con$group2 & mask
      } else {
         group1 <- con$group2 & mask
         group2 <- con$group1 & mask
      }
      sites <- as.numeric(rownames(report))
      extra <- mask & !(group1 | group2)
      allsamps <- c(which(group1), which(group2), which(extra))
      numsamps <- length(allsamps)
      domap <- matrix(0,length(sites),0)
      if(sum(group1)) {     
         domap <- cbind(domap,pv$binding[sites,3+which(group1)])
      }
      if(sum(group2)) {
         domap <- cbind(domap,pv$binding[sites,3+which(group2)])
      }
      if(sum(extra)) {
         domap <- cbind(domap,pv$binding[sites,3+which(extra)])
      }
      rownames(domap) <- rownames(report)
      colnames(domap) <- pv$class[PV_ID,allsamps] 
      con$group1 <- group1
      con$group2 <- group2
      peaks <- pv$peaks[allsamps]
      for(i in 1:length(peaks)){
         peaks[[i]] <- peaks[[i]][sites,]
      }  
   } else {
      if(!bFlip) {
         group1 <- con$group1 
         group2 <- con$group2 
      } else {
         group1 <- con$group2 
         group2 <- con$group1 
      }
      allsamps <- c(which(group1),which(group2))
      extra <- rep(F,ncol(pv$class)) 
      repcols <- colnames(report)
      numsamps <- sum(group1)+sum(group2)
      if(length(repcols) < (numsamps+9)) {
         stop('Report does not have count data, re-run dba.report with bCounts=TRUE')
      }
      first <- 10
      if(repcols[10]=="Called1") {
         if(length(repcols) < (numsamps+11)) {
            stop('Report does not have count data, re-run dba.report with bCounts=TRUE')
         }
         first <- 12	 
      }
      domap <- report[,first:(first+numsamps-1)]
      group1 <- rep(F,numsamps)
      group2 <- rep(T,numsamps)
      group1[1:sum(con$group1)] <- T
      group2[1:sum(con$group1)] <- F
      con$group1 <- group1
      con$group2 <- group2
      
      sites <- as.numeric(rownames(report))
      peaks <- pv$peaks[allsamps]
      for(i in 1:length(peaks)){
         peaks[[i]] <- peaks[[i]][sites,]
      }  
   }
   
   if(bLog) {
      domap[domap<=0]=1
      domap <- log2(domap)
      if(missing(minval)) {
         minval <- 0
      } else {
         minval <- max(0,minval)
      }
   }
   
   if(!missing(minval)) {
      domap[domap< minval]= minval
   }
   if(!missing(maxval)) {
      domap[domap>maxval] <- maxval
   }
   
   pv$binding <- cbind(report[,1:3],domap)
   pv$class <- pv$class[,allsamps]
   pv$peaks <- peaks
   pv$called <- pv$called[sites,allsamps]
   pv$merged <- pv$binding[,1:3]
   pv$totalMerged <- nrow(pv$merged)
   pv$contrasts <- list(pv$contrasts[[contrast]])
   pv$contrasts[[1]]$group1 <- rep(F,ncol(pv$class))
   pv$contrasts[[1]]$group2 <- rep(F,ncol(pv$class))
   if(sum(con$group1)) {
      pv$contrasts[[1]]$group1[1:sum(con$group1)]=T
   }
   if(sum(con$group2)) {
      pv$contrasts[[1]]$group2[(sum(con$group1)+1):(sum(con$group1)+sum(con$group2))]=T   
   }
   
   if(bPCA)  {
      
      #pv$pc <- princomp(domap,cor=bPCAcor)
      
      if(attributes[1] == PV_GROUP) {
         pv$class[PV_ID,] <- c( rep(con$name1,sum(con$group1)), rep(con$name2,sum(con$group2)), rep("other",sum(extra)) )
      }
   } 
   
   return(pv)     
}


pv.get_reads <- function(pv,peaksets,bSubControl=T){
   if(is.null(bSubControl)) {
      bSubControl <- T
   }
   reads <- NULL
   if(!is.null(pv$peaks_alt)) {
      peaklist <- pv$peaks_alt
   } else {
      peaklist <- pv$peaks
   }
   for(peakset in peaksets) {
      reads <- cbind(reads,peaklist[[peakset]]$Reads)
      if(bSubControl) {
         reads[,ncol(reads)] <- reads[,ncol(reads)] - peaklist[[peakset]]$cReads
      }
   }
   
   reads[reads<1]=1
   
   return(reads)
}

pv.getoneorNA <- function(vec) {
   res <- unique(vec)
   if (length(res) == 1) {
      return(res)
   } else {
      return(NA)
   }
}

pv.add_consensus <- function(model,psets){
   
   if(missing(psets)) {
      toadd <- unique(model$class[PV_ID,duplicated(model$class[PV_ID,])])
      for(pset in toadd) {
         tomerge <- which(model$class[PV_ID,] %in% pset)
         model <- pv.consensus(model,tomerge,minOverlap=2)
      }   
      return(model) 
   }
   
   if(is.null(psets)) {
      return(model)
   }
   
   model$psets <- psets
   for(pset in psets){
      if(length(pset)>1){
         model <- pv.consensus(model,pset,minOverlap=2)
      }   
   }
   return(model)	
}

pv.removeComp <- function (data,numRemove=0) {
   
   if(numRemove == 0) {
      return(data)
   }
   
   res <- svd(data)
   U   <- res$u
   d   <- res$d
   Vt  <- t(res$v)
   
   tot <- sum(d)
   run <- 0
   for(i in 1:numRemove) {
      run <- run + d[i]
      #cat(i,d[i],d[i]/tot,run/tot,'\n')
      d[i] <- 0
   }	
   M <-  U %*% diag(d) %*% Vt
   return(M)
}

pv.occ2matrix <- function(occ,col=PV_COR,div){
   #occ[,1] <- as.character(occ[,1])
   #occ[,2] <- as.character(occ[,2])
   els <- unique(c(occ[,1],occ[,2]))
   
   nels <- length(els)
   els  <- els[order(els,decreasing=F)]
   res <- matrix(NA,nels,nels)
   
   for(i in 1:nels) {
      for(j in 1:nels) {
         if(i==j) {
            res[i,j] <- 1
         } else {
            entry <- (occ[,1] == els[i]) & (occ[,2] == els[j])
            entry <- entry | (occ[,1] == els[j]) & (occ[,2] == els[i])
            if(sum(entry)>0) {
               res[i,j] <- as.numeric(occ[entry,col])
               if(!missing(div)) {
                  if(div != PV_TOTAL) {
                     todiv <- as.numeric(occ[entry,div])
                  } else {
                     todiv <- as.numeric(occ[entry,PV_ONLYA]) +
                        as.numeric(occ[entry,PV_ONLYB]) +
                        as.numeric(occ[entry,PV_INALL])
                  }
                  res[i,j] <- res[i,j] / todiv
               }
            }
         }
      }
   } 
   
   colnames(res) <- els
   rownames(res) <- els
   return(res)
}

pv.nums2labels <- function(pv,els,atts) {
   if(is.null(atts)) {
      return(els)
   }
   for(i in 1:length(els)) {
      els[i] <- pv.namestrings(pv$class[atts,as.numeric(els[i])])$tstring
   }
   return(els)
}

pv.getOverlapData <- function(pv,contrast,report) {
   con <- pv$contrasts[[contrast]]                       	
   repcols <- colnames(report)
   numsamps <- sum(con$group1)+sum(con$group2)
   if(length(repcols) < (numsamps+9)) {
      return(pv)
   }
   first <- 10
   if(repcols[10]=="Called1") {
      if(length(repcols) < (numsamps+11)) {
         return(pv)
      }
      first <- 12	 
   }
   
   domap <- report[,first:(first+numsamps-1)]
   
   pv$binding    <- cbind(report[,1:3],domap)
   pv$class      <- cbind(pv$class[,con$group1],pv$class[,con$group2])
   
   return(pv)     
}

#PV_CHIP_RPKM      <- 6
#PV_CHIP_READS     <- 7
#PV_CONTROL_RPKM   <- 8
#PV_CONTROL_READS  <- 9


pv.get_scores <- function(pv,peaksets){
   control <- rep(0,nrow(pv$peaks[[peaksets[1]]]))
   scores <- NULL
   for(peakset in peaksets) {
      control <- control + pv$peaks[[peakset]]$cRPKM
      scores <- cbind(scores,pv$peaks[[peakset]]$RPKM)
   }
   control <- control/length(peaksets)
   for(i in 1:ncol(scores)) {
      scores[,i] <- log2(scores[,i] / control)	
   }
   return(scores)
}

pv.attname <- function(attribute,pv=NULL) {
   if(attribute == PV_ID) {
      if(!is.null(pv)) {
         if(!is.null(pv$config$id)) {
            return(pv$config$id)
         }
      }
      return("ID")
   }
   if(attribute == PV_GROUP) {
      if(!is.null(pv)) {
         if(!is.null(pv$config$group)) {
            return(pv$config$group)
         }
      }
      return("Group")
   }
   if(attribute == PV_TISSUE) {
      if(!is.null(pv)) {
         if(!is.null(pv$config$tissue)) {
            return(pv$config$tissue)
         }
      }
      return("Tissue")
   }
   if(attribute == PV_FACTOR) {
      if(!is.null(pv)) {
         if(!is.null(pv$config$factor)) {
            return(pv$config$factor)
         }
      }
      return("Factor")
   }
   if(attribute == PV_CONDITION) {
      if(!is.null(pv)) {
         if(!is.null(pv$config$condition)) {
            return(pv$config$condition)
         }
      }
      return("Condition")
   }
   if(attribute == PV_TREATMENT) {
      if(!is.null(pv)) {
         if(!is.null(pv$config$treatment)) {
            return(pv$config$treatment)
         }
      }
      return("Treatment")
   }
   
   if(attribute == PV_REPLICATE) {
      if(!is.null(pv)) {
         if(!is.null(pv$config$replicate)) {
            return(pv$config$replicate)
         }
      }
      return("Replicate")
   }
   if(attribute == PV_CALLER) {
      if(!is.null(pv)) {
         if(!is.null(pv$config$caller)) {
            return(pv$config$caller)
         }
      }
      return("Caller")
   }
   
   if(!is.null(pv)) {
      if(!is.null(pv$config$group)) {
         return(pv$config$group)
      }
   }
   return("Group")
}

pv.attributematrix <- function(pv,mask,contrast,attributes,cols,bReverse=F,bAddGroup=F) {
   
   if(is.null(attributes)){
      attributes=c(PV_TISSUE,PV_FACTOR,PV_CONDITION,PV_TREATMENT,PV_REPLICATE,PV_CALLER)
      if(bAddGroup) {
         attributes=c(PV_GROUP,attributes)	
      }	
   }
   
   classdb <- pv$class[,mask]
   numsamps <- ncol(classdb)
   atts <- NULL
   for(num in length(attributes):1) {
      attribute <- attributes[num]
      if(attribute==PV_GROUP) {
         vals <- NA
         if(!missing(contrast)) {
            if(sum(pv$contrasts[[contrast]]$group1) || 
               sum(pv$contrasts[[contrast]]$group2)) {
               gps <- rep(3,length(pv$contrasts[[contrast]]$group1))
               gps[pv$contrasts[[contrast]]$group1]=1
               gps[pv$contrasts[[contrast]]$group2]=2
               classdb <- rbind(classdb,gps)
               attribute <- nrow(classdb)
               vals <- 1:max(gps)
            }
         } else {
            vals <- NA	
         }
      } else {
         vals <- unique(classdb[attribute,])
      }
      numvals <- suppressWarnings(as.numeric(vals))
      if(!sum(is.na(numvals))) {
         vals <- sort(numvals)
      }
      if ( (sum(!is.na(vals))>1) && (sum(!is.na(vals))<numsamps) ) {
         addcol <- matrix(classdb[attribute,],numsamps,1)
         for(i in 1:length(vals)) {
            #addcol[addcol[,1]==vals[i],1]=cols[i]
            addcol[addcol[,1]==vals[i],1]=i
         }
         colnames(addcol) <- pv.attname(attribute,pv)
         if(bReverse) {
            atts <- cbind(addcol,atts)  
         } else { 	
            atts <- cbind(atts,addcol)  	
         }
      }         	
   }
   if(is.null(atts)) {
      return(NULL)
   }
   attnum <- 1
   for(i in ncol(atts):1) {
      if(is.list(cols)) {
         usecols <- cols[[attnum]]
      } else usecols <- cols
      atts[,i] <- usecols[as.numeric(atts[,i])]
      attnum <- attnum+1
   }
   return(atts)	
}

pv.attributePCA <- function(DBA) {
   
   if(pv.morethanone(DBA,DBA_TISSUE))    return(DBA_TISSUE)	
   if(pv.morethanone(DBA,DBA_FACTOR))    return(DBA_FACTOR)	
   if(pv.morethanone(DBA,DBA_CONDITION)) return(DBA_CONDITION)	
   if(pv.morethanone(DBA,DBA_TREATMENT)) return(DBA_TREATMENT)	
   if(pv.morethanone(DBA,DBA_REPLICATE)) return(DBA_REPLICATE)	
   if(pv.morethanone(DBA,DBA_CALLER))    return(DBA_CALLER)
   
   return(DBA_ID)
}

pv.morethanone <- function(DBA,att){
   
   vals <- unique(DBA$class[att,])
   
   if(sum(!is.na(vals))>=2) {
      return(TRUE)
   } else {
      return(FALSE)	
   }	
}

pv.checkValue <- function(val,check) {
   if(is.null(val)) {
      return(FALSE)
   } 
   if (val != check) {
      return(FALSE)
   }
   return(TRUE)
}

pv.setMask <- function(pv,mask,contrast) {
   if(missing(contrast)) {
      newmask <- rep(T,length(pv$peaks))
   } else {
      if(contrast <= length(pv$contrasts)) {
         newmask <- pv$contrasts[[contrast]]$group1 | pv$contrasts[[contrast]]$group2
      } else {
         stop('Invalid contrast',call.=FALSE)
      }
   }
   if(missing(mask)) {
      mask <- newmask
   } else if(is.null(mask)) {
      mask <- newmask   
   } else {
      if(!is.logical(mask)) {
         tmp  <- rep(F,length(pv$peaks))
         tmp[mask] <- T
         mask <- tmp
      }
   }
   if(length(mask) != length(pv$peaks)) {
      stop('Mask does not match samples.')
   }
   for(pnum in 1:length(pv$peaks)) {
      if(nrow(pv$peaks[[pnum]])==0) {
         mask[pnum]=F
      }
   }
   return(mask)
}



### Thanks to Obi Griffith 
### Obtained at http://www.biostars.org/post/show/18211/how-do-i-draw-a-heatmap-in-r-with-both-a-color-key-and-multiple-color-side-bars/
### Downloaded from http://dl.dropbox.com/u/16769159/Biostar/heatmap.3.R
checkinvalid = function (x) 
{
   if (missing(x) || is.null(x) || length(x) == 0) 
      return(TRUE)
   if (is.list(x)) 
      return(all(sapply(x, checkinvalid)))
   else if (is.vector(x)) 
      return(all(is.na(x)))
   else return(FALSE)
}


heatmap.3=function (x, Rowv = TRUE, Colv = if (symm) "Rowv" else TRUE, 
                    distfun = dist, hclustfun = hclust, dendrogram = c("both", 
                                                                       "row", "column", "none"), symm = FALSE, scale = c("none", 
                                                                                                                         "row", "column"), na.rm = TRUE, revC = identical(Colv, 
                                                                                                                                                                          "Rowv"), add.expr, breaks, symbreaks = min(x < 0, na.rm = TRUE) || 
                       scale != "none", col = "heat.colors", colsep, rowsep, 
                    sepcolor = "white", sepwidth = c(0.05, 0.05), cellnote, notecex = 1, 
                    notecol = "cyan", na.color = par("bg"), trace = c("column", 
                                                                      "row", "both", "none"), tracecol = "cyan", hline = median(breaks), 
                    vline = median(breaks), linecol = tracecol, margins = c(5, 
                                                                            5), ColSideColors, RowSideColors, cexRow = 0.2 + 1/log10(nr), 
                    cexCol = 0.2 + 1/log10(nc), labRow = NULL, labCol = NULL, 
                    key = TRUE, keysize = 1.5, density.info = c("histogram", 
                                                                "density", "none"), denscol = tracecol, symkey = min(x < 
                                                                                                                        0, na.rm = TRUE) || symbreaks, densadj = 0.25, main = NULL, 
                    xlab = NULL, ylab = NULL, lmat = NULL, lhei = NULL, lwid = NULL,
                    NumColSideColors = 1, NumRowSideColors = 1, KeyValueName="Value",
                    ...) 
{
   scale01 <- function(x, low = min(x), high = max(x)) {
      x <- (x - low)/(high - low)
      x
   }
   retval <- list()
   scale <- if (symm && missing(scale)) 
      "none"
   else match.arg(scale)
   dendrogram <- match.arg(dendrogram)
   trace <- match.arg(trace)
   density.info <- match.arg(density.info)
   if (length(col) == 1 && is.character(col)) 
      col <- get(col, mode = "function")
   if (!missing(breaks) && (scale != "none")) 
      warning("Using scale=\"row\" or scale=\"column\" when breaks are", 
              "specified can produce unpredictable results.", "Please consider using only one or the other.")
   if (is.null(Rowv) || is.na(Rowv)) 
      Rowv <- FALSE
   if (is.null(Colv) || is.na(Colv)) 
      Colv <- FALSE
   else if (Colv == "Rowv" && !isTRUE(Rowv)) 
      Colv <- FALSE
   if (length(di <- dim(x)) != 2 || !is.numeric(x)) 
      stop("`x' must be a numeric matrix")
   nr <- di[1]
   nc <- di[2]
   if (nr <= 1 || nc <= 1) 
      stop("`x' must have at least 2 rows and 2 columns")
   if (!is.numeric(margins) || length(margins) != 2) 
      stop("`margins' must be a numeric vector of length 2")
   if (missing(cellnote)) 
      cellnote <- matrix("", ncol = ncol(x), nrow = nrow(x))
   if (!inherits(Rowv, "dendrogram")) {
      if (((!isTRUE(Rowv)) || (is.null(Rowv))) && (dendrogram %in% 
                                                   c("both", "row"))) {
         if (is.logical(Colv) && (Colv)) 
            dendrogram <- "column"
         else dedrogram <- "none"
         warning("Discrepancy: Rowv is FALSE, while dendrogram is `", 
                 dendrogram, "'. Omitting row dendogram.")
      }
   }
   if (!inherits(Colv, "dendrogram")) {
      if (((!isTRUE(Colv)) || (is.null(Colv))) && (dendrogram %in% 
                                                   c("both", "column"))) {
         if (is.logical(Rowv) && (Rowv)) 
            dendrogram <- "row"
         else dendrogram <- "none"
         warning("Discrepancy: Colv is FALSE, while dendrogram is `", 
                 dendrogram, "'. Omitting column dendogram.")
      }
   }
   if (inherits(Rowv, "dendrogram")) {
      ddr <- Rowv
      rowInd <- order.dendrogram(ddr)
   }
   else if (is.integer(Rowv)) {
      hcr <- hclustfun(distfun(x))
      ddr <- as.dendrogram(hcr)
      ddr <- reorder(ddr, Rowv)
      rowInd <- order.dendrogram(ddr)
      if (nr != length(rowInd)) 
         stop("row dendrogram ordering gave index of wrong length")
   }
   else if (isTRUE(Rowv)) {
      Rowv <- rowMeans(x, na.rm = na.rm)
      hcr <- hclustfun(distfun(x))
      ddr <- as.dendrogram(hcr)
      ddr <- reorder(ddr, Rowv)
      rowInd <- order.dendrogram(ddr)
      if (nr != length(rowInd)) 
         stop("row dendrogram ordering gave index of wrong length")
   }
   else {
      rowInd <- nr:1
   }
   if (inherits(Colv, "dendrogram")) {
      ddc <- Colv
      colInd <- order.dendrogram(ddc)
   }
   else if (identical(Colv, "Rowv")) {
      if (nr != nc) 
         stop("Colv = \"Rowv\" but nrow(x) != ncol(x)")
      if (exists("ddr")) {
         ddc <- ddr
         colInd <- order.dendrogram(ddc)
      }
      else colInd <- rowInd
   }
   else if (is.integer(Colv)) {
      hcc <- hclustfun(distfun(if (symm) 
         x
         else t(x)))
      ddc <- as.dendrogram(hcc)
      ddc <- reorder(ddc, Colv)
      colInd <- order.dendrogram(ddc)
      if (nc != length(colInd)) 
         stop("column dendrogram ordering gave index of wrong length")
   }
   else if (isTRUE(Colv)) {
      Colv <- colMeans(x, na.rm = na.rm)
      hcc <- hclustfun(distfun(if (symm) 
         x
         else t(x)))
      ddc <- as.dendrogram(hcc)
      ddc <- reorder(ddc, Colv)
      colInd <- order.dendrogram(ddc)
      if (nc != length(colInd)) 
         stop("column dendrogram ordering gave index of wrong length")
   }
   else {
      colInd <- 1:nc
   }
   retval$rowInd <- rowInd
   retval$colInd <- colInd
   retval$call <- match.call()
   x <- x[rowInd, colInd]
   x.unscaled <- x
   cellnote <- cellnote[rowInd, colInd]
   if (is.null(labRow)) 
      labRow <- if (is.null(rownames(x))) 
         (1:nr)[rowInd]
   else rownames(x)
   else labRow <- labRow[rowInd]
   if (is.null(labCol)) 
      labCol <- if (is.null(colnames(x))) 
         (1:nc)[colInd]
   else colnames(x)
   else labCol <- labCol[colInd]
   if (scale == "row") {
      retval$rowMeans <- rm <- rowMeans(x, na.rm = na.rm)
      x <- sweep(x, 1, rm)
      retval$rowSDs <- sx <- apply(x, 1, sd, na.rm = na.rm)
      x <- sweep(x, 1, sx, "/")
   }
   else if (scale == "column") {
      retval$colMeans <- rm <- colMeans(x, na.rm = na.rm)
      x <- sweep(x, 2, rm)
      retval$colSDs <- sx <- apply(x, 2, sd, na.rm = na.rm)
      x <- sweep(x, 2, sx, "/")
   }
   if (missing(breaks) || is.null(breaks) || length(breaks) < 
       1) {
      if (missing(col) || is.function(col)) 
         breaks <- 16
      else breaks <- length(col) + 1
   }
   if (length(breaks) == 1) {
      if (!symbreaks) 
         breaks <- seq(min(x, na.rm = na.rm), max(x, na.rm = na.rm), 
                       length = breaks)
      else {
         extreme <- max(abs(x), na.rm = TRUE)
         breaks <- seq(-extreme, extreme, length = breaks)
      }
   }
   nbr <- length(breaks)
   ncol <- length(breaks) - 1
   if (class(col) == "function") 
      col <- col(ncol)
   min.breaks <- min(breaks)
   max.breaks <- max(breaks)
   x[x < min.breaks] <- min.breaks
   x[x > max.breaks] <- max.breaks
   if (missing(lhei) || is.null(lhei)) 
      lhei <- c(keysize, 4)
   if (missing(lwid) || is.null(lwid)) 
      lwid <- c(keysize, 4)
   if (missing(lmat) || is.null(lmat)) {
      lmat <- rbind(4:3, 2:1)
      
      if (!missing(ColSideColors)) {
         if(!is.null(ColSideColors)) {	
            #if (!is.matrix(ColSideColors)) 
            #stop("'ColSideColors' must be a matrix")
            if (!is.character(ColSideColors) || dim(ColSideColors)[1] != 
                nc) 
               stop("'ColSideColors' dim()[2] must be of length ncol(x)")
            lmat <- rbind(lmat[1, ] + 1, c(NA, 1), lmat[2, ] + 1)
            #lhei <- c(lhei[1], 0.2, lhei[2])
            lhei=c(lhei[1], 0.1*NumColSideColors, lhei[2]) 
         }
      }
      if (!missing(RowSideColors)) {
         if(!is.null(RowSideColors)) { 
            #if (!is.matrix(RowSideColors)) 
            #stop("'RowSideColors' must be a matrix")
            if (!is.character(RowSideColors) || dim(RowSideColors)[1] != 
                nr) 
               stop("'RowSideColors' must be a character vector of length nrow(x)")
            lmat <- cbind(lmat[, 1] + 1, c(rep(NA, nrow(lmat) - 1), 1), lmat[,2] + 1)
            #lwid <- c(lwid[1], 0.2, lwid[2])
            lwid <- c(lwid[1], 0.1*NumRowSideColors, lwid[2])
         }
      }
      lmat[is.na(lmat)] <- 0     
   }
   
   if (length(lhei) != nrow(lmat))
      stop("lhei must have length = nrow(lmat) = ", nrow(lmat))
   if (length(lwid) != ncol(lmat))
      stop("lwid must have length = ncol(lmat) =", ncol(lmat))
   op <- par(no.readonly = TRUE)
   on.exit(par(op))
   layout(lmat, widths = lwid, heights = lhei, respect = FALSE)     
   
   if (!missing(RowSideColors)) {
      if (!is.null(RowSideColors)) {
         #if (!is.matrix(RowSideColors)){
         if(revC) {
            xrowInd = rev(rowInd)	
         } else {
            xrowInd = rowInd	
         }	
         if(ncol(RowSideColors)==1) {	
            par(mar = c(margins[1], 0, 0, 0.5))
            image(rbind(1:nr), col = RowSideColors[,1][xrowInd], axes = FALSE)
            axis(1,0,colnames(RowSideColors)[1],las=2,tick=F)
         } 
         else{
            par(mar = c(margins[1], 0, 0, 0.5))
            rsc = RowSideColors[xrowInd, ]
            rsc.colors = matrix()
            rsc.names = names(table(rsc))
            rsc.i = 1
            for (rsc.name in rsc.names) {
               rsc.colors[rsc.i] = rsc.name
               rsc[rsc == rsc.name] = rsc.i
               rsc.i = rsc.i + 1
            }
            rsc = matrix(as.numeric(rsc), nrow = dim(rsc)[1])
            image(t(rsc), col = as.vector(rsc.colors), axes = FALSE)
            if (length(colnames(RowSideColors)) > 0) {
               axis(1, 0:(dim(rsc)[2] - 1)/(dim(rsc)[2] - 1),
                    colnames(RowSideColors), 
                    las = 2, tick = FALSE)
            }
         }
      }
   }
   
   if (!missing(ColSideColors)) {
      if (!is.null(ColSideColors)) {
         #if (!is.matrix(ColSideColors)){
         if(ncol(ColSideColors)==1) {
            par(mar = c(0.5, 0, 0, margins[2]))
            image(cbind(1:nc), col = ColSideColors[,1][colInd], axes = FALSE)
            axis(2,0,colnames(ColSideColors)[1],las=2,tick=F)
         }
         else {    
            par(mar = c(0.5, 0, 0, margins[2]))
            csc = ColSideColors[colInd, ]
            csc.colors = matrix()
            csc.names = names(table(csc))
            csc.i = 1
            for (csc.name in csc.names) {
               csc.colors[csc.i] = csc.name
               csc[csc == csc.name] = csc.i
               csc.i = csc.i + 1
            }
            csc = matrix(as.numeric(csc), nrow = dim(csc)[1])
            image(csc, col = as.vector(csc.colors), axes = FALSE)
            if (length(colnames(ColSideColors)) > 0) {
               axis(2, 0:(dim(csc)[2] - 1)/(dim(csc)[2] - 1),
                    colnames(ColSideColors), 
                    las = 2, tick = FALSE)
            }
         }
      }
   }
   par(mar = c(margins[1], 0, 0, margins[2]))
   x <- t(x)
   cellnote <- t(cellnote)
   if (revC) {
      iy <- nr:1
      if (exists("ddr")) 
         ddr <- rev(ddr)
      x <- x[, iy]
      cellnote <- cellnote[, iy]
   }
   else iy <- 1:nr
   image(1:nc, 1:nr, x, xlim = 0.5 + c(0, nc), ylim = 0.5 + 
            c(0, nr), axes = FALSE, xlab = "", ylab = "", col = col, 
         breaks = breaks, ...)
   retval$carpet <- x
   if (exists("ddr")) 
      retval$rowDendrogram <- ddr
   if (exists("ddc")) 
      retval$colDendrogram <- ddc
   retval$breaks <- breaks
   retval$col <- col
   if (!checkinvalid(na.color) & any(is.na(x))) {
      mmat <- ifelse(is.na(x), 1, NA)
      image(1:nc, 1:nr, mmat, axes = FALSE, xlab = "", ylab = "", 
            col = na.color, add = TRUE)
   }
   axis(1, 1:nc, labels = labCol, las = 2, line = -0.5, tick = 0, 
        cex.axis = cexCol)
   if (!is.null(xlab)) 
      mtext(xlab, side = 1, line = margins[1] - 1.25)
   axis(4, iy, labels = labRow, las = 2, line = -0.5, tick = 0, 
        cex.axis = cexRow)
   if (!is.null(ylab)) 
      mtext(ylab, side = 4, line = margins[2] - 1.25)
   if (!missing(add.expr)) 
      eval(substitute(add.expr))
   if (!missing(colsep)) 
      for (csep in colsep) rect(xleft = csep + 0.5, ybottom = rep(0, 
                                                                  length(csep)), xright = csep + 0.5 + sepwidth[1], 
                                ytop = rep(ncol(x) + 1, csep), lty = 1, lwd = 1, 
                                col = sepcolor, border = sepcolor)
   if (!missing(rowsep)) 
      for (rsep in rowsep) rect(xleft = 0, ybottom = (ncol(x) + 
                                                         1 - rsep) - 0.5, xright = nrow(x) + 1, ytop = (ncol(x) + 
                                                                                                           1 - rsep) - 0.5 - sepwidth[2], lty = 1, lwd = 1, 
                                col = sepcolor, border = sepcolor)
   min.scale <- min(breaks)
   max.scale <- max(breaks)
   x.scaled <- scale01(t(x), min.scale, max.scale)
   if (trace %in% c("both", "column")) {
      retval$vline <- vline
      vline.vals <- scale01(vline, min.scale, max.scale)
      for (i in colInd) {
         if (!is.null(vline)) {
            abline(v = i - 0.5 + vline.vals, col = linecol, 
                   lty = 2)
         }
         xv <- rep(i, nrow(x.scaled)) + x.scaled[, i] - 0.5
         xv <- c(xv[1], xv)
         yv <- 1:length(xv) - 0.5
         lines(x = xv, y = yv, lwd = 1, col = tracecol, type = "s")
      }
   }
   if (trace %in% c("both", "row")) {
      retval$hline <- hline
      hline.vals <- scale01(hline, min.scale, max.scale)
      for (i in rowInd) {
         if (!is.null(hline)) {
            abline(h = i + hline, col = linecol, lty = 2)
         }
         yv <- rep(i, ncol(x.scaled)) + x.scaled[i, ] - 0.5
         yv <- rev(c(yv[1], yv))
         xv <- length(yv):1 - 0.5
         lines(x = xv, y = yv, lwd = 1, col = tracecol, type = "s")
      }
   }
   if (!missing(cellnote)) 
      text(x = c(row(cellnote)), y = c(col(cellnote)), labels = c(cellnote), 
           col = notecol, cex = notecex)
   par(mar = c(margins[1], 0, 0, 0))
   if (dendrogram %in% c("both", "row")) {
      plot(ddr, horiz = TRUE, axes = FALSE, yaxs = "i", leaflab = "none")
   }
   else plot.new()
   par(mar = c(0, 0, if (!is.null(main)) 5 else 0, margins[2]))
   if (dendrogram %in% c("both", "column")) {
      plot(ddc, axes = FALSE, xaxs = "i", leaflab = "none")
   }
   else plot.new()
   if (!is.null(main)) 
      title(main, cex.main = 1.5 * op[["cex.main"]])
   if (key) {
      par(mar = c(5, 4, 2, 1), cex = 0.75)
      tmpbreaks <- breaks
      if (symkey) {
         max.raw <- max(abs(c(x, breaks)), na.rm = TRUE)
         min.raw <- -max.raw
         tmpbreaks[1] <- -max(abs(x))
         tmpbreaks[length(tmpbreaks)] <- max(abs(x))
      }
      else {
         min.raw <- min(x, na.rm = TRUE)
         max.raw <- max(x, na.rm = TRUE)
      }
      z <- seq(min.raw, max.raw, length = length(col))
      image(z = matrix(z, ncol = 1), col = col, breaks = tmpbreaks, 
            xaxt = "n", yaxt = "n")
      par(usr = c(0, 1, 0, 1))
      lv <- pretty(breaks)
      xv <- scale01(as.numeric(lv), min.raw, max.raw)
      axis(1, at = xv, labels = lv)
      if (scale == "row") 
         mtext(side = 1, "Row Z-Score", line = 2)
      else if (scale == "column") 
         mtext(side = 1, "Column Z-Score", line = 2)
      else mtext(side = 1, KeyValueName, line = 2)
      if (density.info == "density") {
         dens <- density(x, adjust = densadj, na.rm = TRUE)
         omit <- dens$x < min(breaks) | dens$x > max(breaks)
         dens$x <- dens$x[-omit]
         dens$y <- dens$y[-omit]
         dens$x <- scale01(dens$x, min.raw, max.raw)
         lines(dens$x, dens$y/max(dens$y) * 0.95, col = denscol, 
               lwd = 1)
         axis(2, at = pretty(dens$y)/max(dens$y) * 0.95, pretty(dens$y))
         title("Color Key\nand Density Plot")
         par(cex = 0.5)
         mtext(side = 2, "Density", line = 2)
      }
      else if (density.info == "histogram") {
         h <- hist(x, plot = FALSE, breaks = breaks)
         hx <- scale01(breaks, min.raw, max.raw)
         hy <- c(h$counts, h$counts[length(h$counts)])
         lines(hx, hy/max(hy) * 0.95, lwd = 1, type = "s", 
               col = denscol)
         axis(2, at = pretty(hy)/max(hy) * 0.95, pretty(hy))
         title("Color Key\nand Histogram")
         par(cex = 0.5)
         mtext(side = 2, "Count", line = 2)
      }
      else title("Color Key")
   }
   else plot.new()
   retval$colorTable <- data.frame(low = retval$breaks[-length(retval$breaks)], 
                                   high = retval$breaks[-1], color = retval$col)
   invisible(retval)
}

pv.check1 <- function(sbMatrix) {
   if(!is.null(sbMatrix)){
      if(is.null(nrow(sbMatrix))) {
         snames <- names(sbMatrix)
         sbMatrix <- matrix(sbMatrix,1,length(sbMatrix))
         colnames(sbMatrix) <- snames
      }
   }
   return(sbMatrix)
}   
Bioconductor-mirror/DiffBind documentation built on July 14, 2017, 7:48 a.m.