R/CNV_htmp_glist.R
In alimahdipour/sciCNV: sciCNV
Defines functions
CNV_htmp_glist
Documented in
CNV_htmp_glist
#' CNV_htmp_glist function
#'
#' @description Generates a heatmap of single cell CNV profiles plotted by gene list
#'
#' @param CNVmat copy number variation matrix
#' @param Gen.Loc Genomic Location matrix with list of genes, their associated chromosome numbers, starts and ends
#' @param clustering a TRUE/FALSE variable specifying whether to cluster cells based on their CNV similarities. Default is "FALSE"
#' @param clustering.type: variable specifying the method that will be used to cluster sciCNV profiles (cells), if enabled. Possible options are "pearson",
#' "euclidean", " spearman", ... "original" (retain the same cell order/clusters as original without further clustering).
#' Default is "pearson". However this is only enabled when clustering = "TRUE"
#' @param sorting: a TRUE/FALSE variable that enables sorting of cells based on their tumor CNV score from the largest to smallest tumor scores. Default is FASLE.
#' @param CNVscore: the CNV score matrix for all cells (optionally divided by pre-determined clusters). Used only when sorting.clusters = TRUE.
#' @param cluster.lines: is an optional list of values demarcating clusters of cells within the population; only used if multiple
#' cell clusters exist beyond the simple division between test and control populations
#' @param breakGlist: is a set of values each defines a vertical line that separates chromosomes
#' @param No.test: the number of test cells included in the data; can be used to delineate distinct populations of
#' of test annd control cells in the heatmap
#'
#' @return The output is the heatmap of sciCNV matrix for test and control cells against list of genes
#'
#' @examples
#'
#' data(breakGlist)
#' file.path <- system.file("extdata", "CNV_matrix.txt", package="sciCNV")
#' CNVmat <- read.table(file.path, sep="\t", header=TRUE)
#'
#' CNV_htmp_glist(CNVmat=CNVmat, breakGlist=breakGlist, sorting = FALSE, No.test=20, No.normal=20)
#'
#' @import stats
#' @import robustbase
#' @import dichromat
#' @import graphics
#'
#' @export
CNV_htmp_glist <- function(CNVmat,
clustering = FALSE, # TRUE or FALSE
clustering.type = c("pearson", "kendall", "spearman"), # "pearson", "kendalln", " spearman", defualt: "pearson"
sorting = FALSE, # TRUE or FALSE
CNVscore = NULL, # Only used when sorting = TRUE
cluster.lines = NULL,
breakGlist, # separation lines for chromosomes
No.test,
No.normal
){
genelist <- CNVmat[,1]
CNVmat <- as.matrix(CNVmat[,-1])
rownames(CNVmat) <- genelist
## argument validation
if ( missing(sorting) ){
sorting <- FALSE
}
if ( is.null(clustering) ){
clustering <- FALSE
}
if ( clustering == "TRUE" ){
if ( ! clustering.type %in% c("pearson", "kendall", "spearman") ){
stop("Please choose a proper method for unsupervised clustering.")
} else if ( is.null(clustering.type) ){
clustering.type <- c("pearson")
}
if ( sorting == "TRUE" ){
stop("Sorting can occur when clustering = FALSE.")
}
} else if (clustering == "FALSE"){
if ( (sorting == "TRUE") & Reduce("|", is.null(CNVscore)) ){
stop("Please insert a list of CNV-scores")
}
if ( (sorting == "FALSE") & Reduce("|", ! is.null(CNVscore)) ){
stop("Please change sorting status to TRUE to sort data based on CNVscore vector.")
}
}
if ( (is.null(No.test)) & Reduce("|", is.null(cluster.lines)) ){
stop("Please insert the number of test cells (No.test).")
}
if ( Reduce("|", is.null(cluster.lines)) ){
cluster.lines <- c(0, nrow(CNVmat) - No.test, nrow(CNVmat))
}
if ( Reduce("|", is.null(breakGlist)) ){
breakGlist <- c(0, ncol(CNVmat))
}
##### sorting of cells within each cluster by CNV-score, from the largest to the smallest (if applicable)
#nr <- dim(CNVmat)[1]
seq1 <- seq_len(No.test)
seq2 <- No.test + seq_len(No.normal)
if ( sorting == TRUE ){
tst.score <- base::sort(CNVscore[1, seq1] , decreasing=TRUE) #MMPCs
ctrl.score <- base::sort(CNVscore[1, seq2] , decreasing=TRUE) #NBCs
ranked.col <- as.matrix( c(colnames(t(as.matrix(ctrl.score))), colnames(t(as.matrix(tst.score)) )) )
CNV.mat1 <- as.matrix( CNVmat[match(ranked.col, rownames(CNVmat)), ])
rownames(CNV.mat1) <- ranked.col
} else if ( clustering == TRUE ){
if ( is.na(clustering.type) ){
CNV.mat.tst <- as.matrix(CNVmat[seq1, ])
hclst <- stats::hclust(stats::as.dist(1-stats::cor( t(CNV.mat.tst), method = "pearson")), method = "ward.D2")
hclst.lables <- hclst$labels[hclst$order]
CNV.mat.clustered <- CNV.mat.tst[hclst.lables , ]
} else if ( ! missing(clustering.type) ){
CNV.mat.tst <- as.matrix(CNVmat[seq1, ])
hclst <- stats::hclust(stats::as.dist(1-stats::cor( t(CNV.mat.tst), method = clustering.type)), method = "ward.D2")
hclst.lables <- hclst$labels[hclst$order]
CNV.mat.clustered <- CNV.mat.tst[hclst.lables , ]
}
CNVmat.seq2 <- CNVmat[seq2, ]
CNV.mat1 <- rbind(CNVmat.seq2,CNV.mat.clustered)
rownames(CNV.mat1) <- c(rownames(CNVmat.seq2), hclst.lables)
} else if ( (clustering == "FALSE" ) & ( sorting == "FALSE")){
CNVmat.seq1 <- CNVmat[seq1, ]
CNVmat.seq2 <- CNVmat[seq2, ]
CNV.mat1 <- rbind(CNVmat.seq2,CNVmat.seq1)
rownames(CNV.mat1) <- c(rownames(CNVmat.seq2),rownames(CNVmat.seq1))
colnames(CNV.mat1) <- colnames(CNVmat)
}
ROWlist <- rownames(CNV.mat1)
COLlist <- colnames(CNV.mat1)
## converging single cell copy number values towards integers
LL1 <- 0.5
LL2 <- 1.5
LL3 <- 2.5
CNV.mat11 <- matrix(0, ncol = ncol(CNV.mat1), nrow = nrow(CNV.mat1))
for (w in seq_len(ncol(CNV.mat1))){
for(l in seq_len(nrow(CNV.mat1))){
if( abs(CNV.mat1[ l, w]) < LL1 ){
CNV.mat11[ l, w] <- sign(CNV.mat1[ l, w])*( CNV.mat1[ l, w] )^2
} else if((abs(CNV.mat1[ l, w]) >= LL1) & (abs(CNV.mat1[ l, w]) <= LL2)){
CNV.mat11[ l, w] <- sign(CNV.mat1[ l, w])*sqrt( abs(CNV.mat1[ l, w]) )
} else if(abs(CNV.mat1[ l, w]) > LL2 ){
CNV.mat11[ l, w] <- 2*sign(CNV.mat1[ l, w])*sqrt( abs(CNV.mat1[ l, w]) )
}
}
}
#-------
CNV.mat1 <- as.matrix(CNV.mat11)
LL <- 1
TT <- 0.5
CNV.mat1[which(CNV.mat1 > LL)] <- LL
CNV.mat1[which( (CNV.mat1 < TT) & (CNV.mat1 > -TT) )] <- 0.0
CNV.mat1[which(CNV.mat1 < -LL)] <- -LL
CNV.mat <- as.matrix(CNV.mat1)
rownames(CNV.mat) <- matrix(NA, ncol=1, nrow=nrow(CNV.mat1))
##################################################
## Heatmap of sciCNV profiles plotted by gene list
##################################################
## Using k-nearest neighbors (kNN)-method
CNV.mat31 <- as.matrix(CNV.mat)
CNV.mat3 <- matrix(0, ncol=ncol(CNV.mat31), nrow=nrow(CNV.mat31))
Orig <- 0.5
LLength <- c(0, cluster.lines[ 2:( length(cluster.lines) ) ])
for(j in seq_len( length(LLength)-1)){
NeighborNo <- min(20, floor((LLength[j+1]-LLength[j])/2))
for(i in LLength[j]+seq_len(LLength[j]+floor(NeighborNo)-LLength[j])){
CNV.mat3[i, ] <- CNV.mat31[i, ]*Orig + (1-Orig)*robustbase::colMedians(CNV.mat31[ setdiff(seq(LLength[j]+1,(i+NeighborNo),1),i), ])
}
if( (LLength[j]+NeighborNo+1) <= (LLength[j+1]-(NeighborNo) )){
for(i in LLength[j]+NeighborNo+seq_len(LLength[j+1]-2*NeighborNo -LLength[j])){
CNV.mat3[i, ] <- CNV.mat31[i, ]*Orig + (1-Orig)*robustbase::colMedians( CNV.mat31[ setdiff(seq(i-NeighborNo,i+NeighborNo,1),i), ])
}
}
for(i in LLength[j+1]-NeighborNo+seq_len(NeighborNo)){
CNV.mat3[i, ] <- CNV.mat31[i, ]*Orig + (1-Orig)*robustbase::colMedians(CNV.mat31[setdiff(seq(i-NeighborNo,LLength[j+1], 1),i), ])
}
}
CNV.mat3 <- as.matrix(CNV.mat3)
## Assigning x-axis location to gene-centered data based on genomic location rank
label.glist <- t(as.matrix(c(paste("Chr", 1:22, sep = ""),"ChrX", "ChrY")))
label.call.glist <- rep(NA, ncol(CNVmat))
breakglist <- unlist(as.list(t(breakGlist)))
for(i in seq_len(22)){
if( breakglist[i] < ncol(CNVmat) - 10){
label.call.glist[breakglist[i]+10 ] <- as.matrix(label.glist[i])
}
}
if( breakglist[23] < ncol(CNVmat) ){
label.call.glist[breakglist[23] ] <- as.matrix(label.glist[23])
label.call.glist[breakglist[24] ] <- as.matrix(label.glist[24])
} else {
label.call.glist[breakglist[23] ] <- as.matrix(label.glist[23])
}
rownames(CNV.mat3) <- ROWlist
final.mat <- CNV.mat3
## Sketching the heatmap
COL_vec <- c( rep("steelblue",1), rep("white",2),rep("firebrick",1) )
if(clustering == TRUE){
Separns <- c(1,nrow(final.mat)-No.test,nrow(final.mat))
graphics::plot.new()
graphics::par(mar=c(5,5,4,2)+1,mgp=c(3,1,0))
heatmap.3( CNV.mat.clustered ,
main = "Heatmap of sciCNV profiles of test and control cells
Thr 0.5 of 1",
xlab="Genomic location of expressed genes",
ylab= "Cells",
breaks = seq(-LL, LL, length.out =16),
col = colorRampPalette(COL_vec, space = "rgb")(15),
Colv = "NA",
trace="none",
treeheight_row = 0.2,
sepwidth=c(0.2,0.2),
sepcolor = "black",
scale= "none",
labRaw = NA,
labCol = label.call.glist,
Rowv = TRUE,
dendrogram = "row",
cluster.by.row = TRUE,
hclust.FUN = hclst,
cexCol = 1,
srtCol=90,
cutree_rows = 2,
hieght=50, width = 400,
legend = TRUE,
margins = c(4,2),
key.xlab = "Transcription level",
denscol = "grey",
density.info = "density",
rowsep= Separns,
add.expr = graphics::abline(v = c(breakglist))
)
} else {
graphics::plot.new()
graphics::par(mar=c(5,5,4,2)+1,mgp=c(3,1,0))
heatmap.3( final.mat ,
main = paste("Heatmap of sciCNV profiles of test and control cells
Thr 0.5 of 1 -",NeighborNo," nearst neighbors", sep="" ),
xlab = "Genomic location of expressed genes",
ylab= "Cells",
breaks = seq(-LL, LL, length.out =16),
col = colorRampPalette(COL_vec, space = "rgb")(15),
Rowv = FALSE,
Colv = FALSE,
trace ="none",
sepwidth = c(0.2,0.2),
sepcolor = "black",
scale = "none",
labRow = NA,
labCol = label.call.glist,
cexCol = 1,
srtCol = 90,
hieght = 50,
width = 400,
legend = TRUE,
margins = c(4,2),
key.xlab = "Transcription level",
denscol = "grey",
density.info = "density",
rowsep = cluster.lines ,
add.expr = graphics::abline(v = c(breakglist)))
}
}
alimahdipour/sciCNV documentation built on Oct. 16, 2022, 12:56 p.m.
R Package Documentation
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Defines functions CNV_htmp_glist
Documented in CNV_htmp_glist
#' CNV_htmp_glist function
#'
#' @description Generates a heatmap of single cell CNV profiles plotted by gene list
#'
#' @param CNVmat copy number variation matrix
#' @param Gen.Loc Genomic Location matrix with list of genes, their associated chromosome numbers, starts and ends
#' @param clustering a TRUE/FALSE variable specifying whether to cluster cells based on their CNV similarities. Default is "FALSE"
#' @param clustering.type: variable specifying the method that will be used to cluster sciCNV profiles (cells), if enabled. Possible options are "pearson",
#' "euclidean", " spearman", ... "original" (retain the same cell order/clusters as original without further clustering).
#' Default is "pearson". However this is only enabled when clustering = "TRUE"
#' @param sorting: a TRUE/FALSE variable that enables sorting of cells based on their tumor CNV score from the largest to smallest tumor scores. Default is FASLE.
#' @param CNVscore: the CNV score matrix for all cells (optionally divided by pre-determined clusters). Used only when sorting.clusters = TRUE.
#' @param cluster.lines: is an optional list of values demarcating clusters of cells within the population; only used if multiple
#' cell clusters exist beyond the simple division between test and control populations
#' @param breakGlist: is a set of values each defines a vertical line that separates chromosomes
#' @param No.test: the number of test cells included in the data; can be used to delineate distinct populations of
#' of test annd control cells in the heatmap
#'
#' @return The output is the heatmap of sciCNV matrix for test and control cells against list of genes
#'
#' @examples
#'
#' data(breakGlist)
#' file.path <- system.file("extdata", "CNV_matrix.txt", package="sciCNV")
#' CNVmat <- read.table(file.path, sep="\t", header=TRUE)
#'
#' CNV_htmp_glist(CNVmat=CNVmat, breakGlist=breakGlist, sorting = FALSE, No.test=20, No.normal=20)
#'
#' @import stats
#' @import robustbase
#' @import dichromat
#' @import graphics
#'
#' @export
CNV_htmp_glist <- function(CNVmat,
clustering = FALSE, # TRUE or FALSE
clustering.type = c("pearson", "kendall", "spearman"), # "pearson", "kendalln", " spearman", defualt: "pearson"
sorting = FALSE, # TRUE or FALSE
CNVscore = NULL, # Only used when sorting = TRUE
cluster.lines = NULL,
breakGlist, # separation lines for chromosomes
No.test,
No.normal
){
genelist <- CNVmat[,1]
CNVmat <- as.matrix(CNVmat[,-1])
rownames(CNVmat) <- genelist
## argument validation
if ( missing(sorting) ){
sorting <- FALSE
}
if ( is.null(clustering) ){
clustering <- FALSE
}
if ( clustering == "TRUE" ){
if ( ! clustering.type %in% c("pearson", "kendall", "spearman") ){
stop("Please choose a proper method for unsupervised clustering.")
} else if ( is.null(clustering.type) ){
clustering.type <- c("pearson")
}
if ( sorting == "TRUE" ){
stop("Sorting can occur when clustering = FALSE.")
}
} else if (clustering == "FALSE"){
if ( (sorting == "TRUE") & Reduce("|", is.null(CNVscore)) ){
stop("Please insert a list of CNV-scores")
}
if ( (sorting == "FALSE") & Reduce("|", ! is.null(CNVscore)) ){
stop("Please change sorting status to TRUE to sort data based on CNVscore vector.")
}
}
if ( (is.null(No.test)) & Reduce("|", is.null(cluster.lines)) ){
stop("Please insert the number of test cells (No.test).")
}
if ( Reduce("|", is.null(cluster.lines)) ){
cluster.lines <- c(0, nrow(CNVmat) - No.test, nrow(CNVmat))
}
if ( Reduce("|", is.null(breakGlist)) ){
breakGlist <- c(0, ncol(CNVmat))
}
##### sorting of cells within each cluster by CNV-score, from the largest to the smallest (if applicable)
#nr <- dim(CNVmat)[1]
seq1 <- seq_len(No.test)
seq2 <- No.test + seq_len(No.normal)
if ( sorting == TRUE ){
tst.score <- base::sort(CNVscore[1, seq1] , decreasing=TRUE) #MMPCs
ctrl.score <- base::sort(CNVscore[1, seq2] , decreasing=TRUE) #NBCs
ranked.col <- as.matrix( c(colnames(t(as.matrix(ctrl.score))), colnames(t(as.matrix(tst.score)) )) )
CNV.mat1 <- as.matrix( CNVmat[match(ranked.col, rownames(CNVmat)), ])
rownames(CNV.mat1) <- ranked.col
} else if ( clustering == TRUE ){
if ( is.na(clustering.type) ){
CNV.mat.tst <- as.matrix(CNVmat[seq1, ])
hclst <- stats::hclust(stats::as.dist(1-stats::cor( t(CNV.mat.tst), method = "pearson")), method = "ward.D2")
hclst.lables <- hclst$labels[hclst$order]
CNV.mat.clustered <- CNV.mat.tst[hclst.lables , ]
} else if ( ! missing(clustering.type) ){
CNV.mat.tst <- as.matrix(CNVmat[seq1, ])
hclst <- stats::hclust(stats::as.dist(1-stats::cor( t(CNV.mat.tst), method = clustering.type)), method = "ward.D2")
hclst.lables <- hclst$labels[hclst$order]
CNV.mat.clustered <- CNV.mat.tst[hclst.lables , ]
}
CNVmat.seq2 <- CNVmat[seq2, ]
CNV.mat1 <- rbind(CNVmat.seq2,CNV.mat.clustered)
rownames(CNV.mat1) <- c(rownames(CNVmat.seq2), hclst.lables)
} else if ( (clustering == "FALSE" ) & ( sorting == "FALSE")){
CNVmat.seq1 <- CNVmat[seq1, ]
CNVmat.seq2 <- CNVmat[seq2, ]
CNV.mat1 <- rbind(CNVmat.seq2,CNVmat.seq1)
rownames(CNV.mat1) <- c(rownames(CNVmat.seq2),rownames(CNVmat.seq1))
colnames(CNV.mat1) <- colnames(CNVmat)
}
ROWlist <- rownames(CNV.mat1)
COLlist <- colnames(CNV.mat1)
## converging single cell copy number values towards integers
LL1 <- 0.5
LL2 <- 1.5
LL3 <- 2.5
CNV.mat11 <- matrix(0, ncol = ncol(CNV.mat1), nrow = nrow(CNV.mat1))
for (w in seq_len(ncol(CNV.mat1))){
for(l in seq_len(nrow(CNV.mat1))){
if( abs(CNV.mat1[ l, w]) < LL1 ){
CNV.mat11[ l, w] <- sign(CNV.mat1[ l, w])*( CNV.mat1[ l, w] )^2
} else if((abs(CNV.mat1[ l, w]) >= LL1) & (abs(CNV.mat1[ l, w]) <= LL2)){
CNV.mat11[ l, w] <- sign(CNV.mat1[ l, w])*sqrt( abs(CNV.mat1[ l, w]) )
} else if(abs(CNV.mat1[ l, w]) > LL2 ){
CNV.mat11[ l, w] <- 2*sign(CNV.mat1[ l, w])*sqrt( abs(CNV.mat1[ l, w]) )
}
}
}
#-------
CNV.mat1 <- as.matrix(CNV.mat11)
LL <- 1
TT <- 0.5
CNV.mat1[which(CNV.mat1 > LL)] <- LL
CNV.mat1[which( (CNV.mat1 < TT) & (CNV.mat1 > -TT) )] <- 0.0
CNV.mat1[which(CNV.mat1 < -LL)] <- -LL
CNV.mat <- as.matrix(CNV.mat1)
rownames(CNV.mat) <- matrix(NA, ncol=1, nrow=nrow(CNV.mat1))
##################################################
## Heatmap of sciCNV profiles plotted by gene list
##################################################
## Using k-nearest neighbors (kNN)-method
CNV.mat31 <- as.matrix(CNV.mat)
CNV.mat3 <- matrix(0, ncol=ncol(CNV.mat31), nrow=nrow(CNV.mat31))
Orig <- 0.5
LLength <- c(0, cluster.lines[ 2:( length(cluster.lines) ) ])
for(j in seq_len( length(LLength)-1)){
NeighborNo <- min(20, floor((LLength[j+1]-LLength[j])/2))
for(i in LLength[j]+seq_len(LLength[j]+floor(NeighborNo)-LLength[j])){
CNV.mat3[i, ] <- CNV.mat31[i, ]*Orig + (1-Orig)*robustbase::colMedians(CNV.mat31[ setdiff(seq(LLength[j]+1,(i+NeighborNo),1),i), ])
}
if( (LLength[j]+NeighborNo+1) <= (LLength[j+1]-(NeighborNo) )){
for(i in LLength[j]+NeighborNo+seq_len(LLength[j+1]-2*NeighborNo -LLength[j])){
CNV.mat3[i, ] <- CNV.mat31[i, ]*Orig + (1-Orig)*robustbase::colMedians( CNV.mat31[ setdiff(seq(i-NeighborNo,i+NeighborNo,1),i), ])
}
}
for(i in LLength[j+1]-NeighborNo+seq_len(NeighborNo)){
CNV.mat3[i, ] <- CNV.mat31[i, ]*Orig + (1-Orig)*robustbase::colMedians(CNV.mat31[setdiff(seq(i-NeighborNo,LLength[j+1], 1),i), ])
}
}
CNV.mat3 <- as.matrix(CNV.mat3)
## Assigning x-axis location to gene-centered data based on genomic location rank
label.glist <- t(as.matrix(c(paste("Chr", 1:22, sep = ""),"ChrX", "ChrY")))
label.call.glist <- rep(NA, ncol(CNVmat))
breakglist <- unlist(as.list(t(breakGlist)))
for(i in seq_len(22)){
if( breakglist[i] < ncol(CNVmat) - 10){
label.call.glist[breakglist[i]+10 ] <- as.matrix(label.glist[i])
}
}
if( breakglist[23] < ncol(CNVmat) ){
label.call.glist[breakglist[23] ] <- as.matrix(label.glist[23])
label.call.glist[breakglist[24] ] <- as.matrix(label.glist[24])
} else {
label.call.glist[breakglist[23] ] <- as.matrix(label.glist[23])
}
rownames(CNV.mat3) <- ROWlist
final.mat <- CNV.mat3
## Sketching the heatmap
COL_vec <- c( rep("steelblue",1), rep("white",2),rep("firebrick",1) )
if(clustering == TRUE){
Separns <- c(1,nrow(final.mat)-No.test,nrow(final.mat))
graphics::plot.new()
graphics::par(mar=c(5,5,4,2)+1,mgp=c(3,1,0))
heatmap.3( CNV.mat.clustered ,
main = "Heatmap of sciCNV profiles of test and control cells
Thr 0.5 of 1",
xlab="Genomic location of expressed genes",
ylab= "Cells",
breaks = seq(-LL, LL, length.out =16),
col = colorRampPalette(COL_vec, space = "rgb")(15),
Colv = "NA",
trace="none",
treeheight_row = 0.2,
sepwidth=c(0.2,0.2),
sepcolor = "black",
scale= "none",
labRaw = NA,
labCol = label.call.glist,
Rowv = TRUE,
dendrogram = "row",
cluster.by.row = TRUE,
hclust.FUN = hclst,
cexCol = 1,
srtCol=90,
cutree_rows = 2,
hieght=50, width = 400,
legend = TRUE,
margins = c(4,2),
key.xlab = "Transcription level",
denscol = "grey",
density.info = "density",
rowsep= Separns,
add.expr = graphics::abline(v = c(breakglist))
)
} else {
graphics::plot.new()
graphics::par(mar=c(5,5,4,2)+1,mgp=c(3,1,0))
heatmap.3( final.mat ,
main = paste("Heatmap of sciCNV profiles of test and control cells
Thr 0.5 of 1 -",NeighborNo," nearst neighbors", sep="" ),
xlab = "Genomic location of expressed genes",
ylab= "Cells",
breaks = seq(-LL, LL, length.out =16),
col = colorRampPalette(COL_vec, space = "rgb")(15),
Rowv = FALSE,
Colv = FALSE,
trace ="none",
sepwidth = c(0.2,0.2),
sepcolor = "black",
scale = "none",
labRow = NA,
labCol = label.call.glist,
cexCol = 1,
srtCol = 90,
hieght = 50,
width = 400,
legend = TRUE,
margins = c(4,2),
key.xlab = "Transcription level",
denscol = "grey",
density.info = "density",
rowsep = cluster.lines ,
add.expr = graphics::abline(v = c(breakglist)))
}
}
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