R/heatMaps.R
In joseTamezPena/FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis
Defines functions
heatMaps
Documented in
heatMaps
heatMaps <- function(variableList=NULL,varRank=NULL,Outcome,data,title="Heat Map",hCluster=FALSE,prediction=NULL,Scale=FALSE,theFiveColors=c("blue","cyan","black","yellow","red"),outcomeColors = c("blue","lightgreen","yellow","orangered","red"),transpose=FALSE,...)
{
opg <- par(no.readonly=TRUE)
color.bar <- function(lut, min, max=-min, nticks=3, ticks=seq(min, max, len=nticks),transpose=FALSE, Title='',...)
{
op <- par(no.readonly=TRUE)
par(new=TRUE,cex=0.35,mai=c(0,0,0,0),...)
plot(c(0,5), c(min,max), type='n', bty='n', xaxt='n', xlab='', yaxt='n', ylab='',main='')
dy <- (grconvertY(1.00,"npc")-grconvertY(0.00,"npc"))/(length(lut));
axis(2, ticks, las=1,bty='n',outer=TRUE,labels=ifelse(ticks >= 1, sprintf("%0.1f", ticks),
sprintf("%0.1f", ticks)))
for (i in 1:length(lut))
{
y = dy*(i-1)+min;
rect(0,y,grconvertX(0.75,"npc"),(y+dy), col=lut[i], border=NA)
}
title(Title, line = 0.25,outer=TRUE)
par(op)
}
Rowv = TRUE;
Colv = TRUE;
dendrogram = "both";
if (hCluster == "col")
{
hCluster=FALSE;
dendrogram="col";
Rowv=NULL;
}
if (hCluster == "row")
{
hCluster=FALSE;
dendrogram="row";
Colv=NULL;
}
par(new=FALSE,pty='m')
if (!requireNamespace("gplots", quietly = TRUE)) {
install.packages("gplots", dependencies = TRUE)
}
if (!requireNamespace("RColorBrewer", quietly = TRUE)) {
install.packages("RColorBrewer", dependencies = TRUE)
}
# creates a own color palette from red to blue
my_palette <- colorRampPalette(theFiveColors)(n = 224)
# my_palette <- colorRampPalette(c("blue","light blue","black","pink","red"))(n = 224)
# defines the color breaks manually for a "skewed" color transition
col_breaks = c(
seq(-2.0,-1.101,length=50), # for blue
seq(-1.10,-0.201,length=50), # for cyan
seq(-0.20,0.20,length=25), # for black
seq(0.201,1.10,length=50), # for yellows
seq(1.101,2.0,length=50)) # for red
if (is.null(variableList))
{
variableList <- colnames(data)
names(variableList) <- colnames(data)
}
if (inherits(variableList,"data.frame"))
{
vnames <- as.vector(variableList[,1]);
}
else
{
vnames <- names(variableList[!(names(variableList) %in% Outcome)]);
}
if (length(vnames)>2)
{
frm <- paste(Outcome," ~ ",1);
added = 0;
for (i in 1:length(vnames))
{
if ((vnames[i] != "")&&(vnames[i] != "1"))
{
frm <- paste(frm,"+",vnames[i])
added = added + 1;
}
}
modelFrame <- model.frame(formula(frm),data);
rownames(modelFrame) <- rownames(data)
if (is.null(varRank))
{
topvarID <- seq(1, added, 1)
hits <- seq(1, added, 1)
}
else
{
topvarID <- as.numeric(rownames(varRank));
hits <- as.vector(as.matrix(varRank))
}
orderData <- cbind(data[,Outcome]);
orderData <- as.data.frame(orderData);
rownames(orderData) <- rownames(data);
colnames(orderData) <- c(Outcome);
cn = 1;
if (!is.null(prediction))
{
if (prediction[1]!=Outcome)
{
cn = cn + 1;
orderData <- cbind(orderData,as.vector(prediction));
colnames(orderData)[cn] <- "prediction";
}
}
for ( i in 1:length(topvarID))
{
if (hits[i]>0)
{
orderData <- cbind(orderData,modelFrame[,topvarID[i]+1])
cn = cn + 1;
colnames(orderData)[cn] <- colnames(modelFrame)[topvarID[i]+1];
}
}
dataMat <- as.matrix(orderData);
minout <- min(data[,Outcome]);
maxout <- max(data[,Outcome]);
if (!is.null(prediction))
{
if (prediction[1]!=Outcome)
{
if (transpose)
{
orderData <- eval(parse(text=paste("with(orderData,orderData[order(",Outcome,",orderData[,2]),])")))
}
else
{
orderData <- eval(parse(text=paste("with(orderData,orderData[order(-",Outcome,",-orderData[,2]),])")))
}
}
}
else
{
sortsum <- rowSums (abs(orderData), na.rm = TRUE)
orderData <- eval(parse(text=paste("with(orderData,orderData[order(-",Outcome,",-sortsum),])")))
cm <- as.data.frame(cor(t(orderData[,-1]),method="pearson"));
cm[is.na(cm)] <- -2;
# save(cm,file="cormat.RDATA");
rownames (cm) <- c(1:nrow(cm))
colnames (cm) <- c(1:nrow(cm))
indx <- 1
ix <- 1
rm <- cm;
thenames <- colnames(cm)
for (i in 1:(nrow(cm)-2))
{
rm <- cm[ix,-indx]
ix <- as.integer(names(which.max(rm)))
indx <- append(indx,ix)
}
indx <- append(indx,as.integer(thenames[-indx]))
# cat(indx,"\n")
if (transpose)
{
orderData <- orderData[rev(indx),];
orderData <- eval(parse(text=paste("with(orderData,orderData[order(",Outcome,"),])")))
}
else
{
orderData <- orderData[indx,];
orderData <- eval(parse(text=paste("with(orderData,orderData[order(-",Outcome,"),])")))
}
}
orderData <- as.matrix(orderData);
nr <- nrow(orderData);
index <- floor(nr*(orderData[,1]-minout)/(1.0001*maxout-minout))+1;
rowcolors <- colorRampPalette(outcomeColors)(n = nr)[index];
orderData <- orderData[,-1];
if (inherits(Scale,"logical"))
{
if (Scale) orderData <- scale(orderData);
}
nclass <- length(table(data[,Outcome]))
nticks <- min(5,nclass);
# print(nclass)
colorange = FALSE;
if (inherits(Scale,"logical"))
{
colorange = !Scale;
}
if (inherits(Scale,"numeric"))
{
colorange = TRUE;
}
if (colorange)
{
if (inherits(Scale,"numeric"))
{
kmin <- Scale[1];
kmax <- Scale[2];
}
else
{
kmin <- min(orderData);
kmax <- max(orderData);
}
brange <- (kmax-kmin)/5;
col_breaks = c(
seq(kmin,kmin+0.99*brange,length=45), # for blue
seq(kmin+brange,kmin+1.99*brange,length=45), # for cyan
seq(kmin+2*brange,kmin+2.99*brange,length=45), # for black
seq(kmin+3*brange,kmin+3.99*brange,length=45), # for yellows
seq(kmin+4*brange,kmax,length=45)) # for red
}
if (transpose)
{
if (is.null(prediction))
{
if (hCluster==TRUE)
{
heatMap <- gplots::heatmap.2(t(orderData),
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="both", # raw and column dendrogram
ColSideColors=rowcolors,
# lmat=rbind(c(0,5,0,4,0), c(0,3,1,2,0)),
# lhei=c(1.5,4.5),
# lwid=c(0.10,3,3,2,0.10),
...)
}
else
{
# rownames(orderData) <- format(orderData[,1],digits=4);
heatMap <- gplots::heatmap.2(t(orderData),
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
ColSideColors=rowcolors,
dendrogram=dendrogram,
Colv=Colv,
Rowv=Rowv,
...) # turn off row clustering
}
}
else
{
if (hCluster==FALSE)
{
heatMap <- gplots::heatmap.2(t(orderData),
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram=dendrogram,
Colv=Colv,
Rowv=Rowv,
ColSideColors=rowcolors,
...)
}
else
{
# rownames(orderData) <- format(orderData[,1],digits=4);
# index <- ((orderData[,1]+2)/4)
# index <- index*(index>0);
# index <- index*(index<1)+0.999*(index>=1);
# index <- floor(nrow(orderData)*index)+1;
# rowcolors <- colorRampPalette(theFiveColors)(n = nrow(orderData))[index];
# orderData <- orderData[,-1];
heatMap <- gplots::heatmap.2(t(orderData),
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="col", #
ColSideColors=rowcolors,
# lmat=rbind(c(0,5,0,4,0), c(0,3,1,2,0)),
# lhei=c(1.5,4.5),
# lwid=c(0.10,2,0.25,6.55,0.10),
Rowv="NA", # turn off col clustering
...)
}
}
rowcolors <- colorRampPalette(outcomeColors)(n = min(100,nclass));
color.bar(rowcolors,min=minout,max=maxout,nticks=nticks,Title=Outcome,omd=c(0.96,0.99,0.83,0.95))
}
else
{
if (is.null(prediction))
{
if (hCluster==TRUE)
{
heatMap <- gplots::heatmap.2(orderData,
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="both", # raw and column dendrogram
RowSideColors=rowcolors,
# lmat=rbind(c(0,5,0,4,0), c(0,3,1,2,0)),
# lhei=c(1.5,4.5),
# lwid=c(0.10,2,0.25,6.55,0.10),
...) # turn off column clustering
}
else
{
# rownames(orderData) <- format(orderData[,1],digits=4);
heatMap <- gplots::heatmap.2(orderData,
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram=dendrogram,
Colv=Colv,
Rowv=Rowv,
RowSideColors=rowcolors,
...)
}
}
else
{
if (hCluster==FALSE)
{
heatMap <- gplots::heatmap.2(orderData,
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram=dendrogram,
Colv=Colv,
Rowv=Rowv,
RowSideColors=rowcolors,
...)
}
else
{
# rownames(orderData) <- format(orderData[,1],digits=4);
# index <- ((orderData[,1]+2)/4)
# index <- index*(index>0);
# index <- index*(index<1)+0.999*(index>=1);
# index <- floor(nrow(orderData)*index)+1;
# rowcolors <- colorRampPalette(theFiveColors)(n = nrow(orderData))[index];
# orderData <- orderData[,-1];
heatMap <- gplots::heatmap.2(orderData,
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="row", #
RowSideColors=rowcolors,
# lmat=rbind(c(0,5,0,4,0), c(0,3,1,2,0)),
# lhei=c(1.5,4.5),
# lwid=c(0.10,2,0.25,6.55,0.10),
Colv="NA", # turn off col clustering
...)
}
}
rowcolors <- colorRampPalette(outcomeColors)(n = min(100,nclass));
color.bar(rowcolors,min=minout,max=maxout,nticks=nticks,Title=Outcome,omd=c(0.04,0.07,0.07,0.20))
}
par(opg)
result <- list(dataMatrix=dataMat,
orderMatrix=orderData,
heatMap=heatMap)
}
else
{
cat("No enough features for heatmap\n");
result <- NULL
}
# par(pty='m',mfrow=c(1,1))
return (result);
}
joseTamezPena/FRESA.CAD documentation built on Feb. 13, 2025, 1:37 a.m.
R Package Documentation
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Defines functions heatMaps
Documented in heatMaps
heatMaps <- function(variableList=NULL,varRank=NULL,Outcome,data,title="Heat Map",hCluster=FALSE,prediction=NULL,Scale=FALSE,theFiveColors=c("blue","cyan","black","yellow","red"),outcomeColors = c("blue","lightgreen","yellow","orangered","red"),transpose=FALSE,...)
{
opg <- par(no.readonly=TRUE)
color.bar <- function(lut, min, max=-min, nticks=3, ticks=seq(min, max, len=nticks),transpose=FALSE, Title='',...)
{
op <- par(no.readonly=TRUE)
par(new=TRUE,cex=0.35,mai=c(0,0,0,0),...)
plot(c(0,5), c(min,max), type='n', bty='n', xaxt='n', xlab='', yaxt='n', ylab='',main='')
dy <- (grconvertY(1.00,"npc")-grconvertY(0.00,"npc"))/(length(lut));
axis(2, ticks, las=1,bty='n',outer=TRUE,labels=ifelse(ticks >= 1, sprintf("%0.1f", ticks),
sprintf("%0.1f", ticks)))
for (i in 1:length(lut))
{
y = dy*(i-1)+min;
rect(0,y,grconvertX(0.75,"npc"),(y+dy), col=lut[i], border=NA)
}
title(Title, line = 0.25,outer=TRUE)
par(op)
}
Rowv = TRUE;
Colv = TRUE;
dendrogram = "both";
if (hCluster == "col")
{
hCluster=FALSE;
dendrogram="col";
Rowv=NULL;
}
if (hCluster == "row")
{
hCluster=FALSE;
dendrogram="row";
Colv=NULL;
}
par(new=FALSE,pty='m')
if (!requireNamespace("gplots", quietly = TRUE)) {
install.packages("gplots", dependencies = TRUE)
}
if (!requireNamespace("RColorBrewer", quietly = TRUE)) {
install.packages("RColorBrewer", dependencies = TRUE)
}
# creates a own color palette from red to blue
my_palette <- colorRampPalette(theFiveColors)(n = 224)
# my_palette <- colorRampPalette(c("blue","light blue","black","pink","red"))(n = 224)
# defines the color breaks manually for a "skewed" color transition
col_breaks = c(
seq(-2.0,-1.101,length=50), # for blue
seq(-1.10,-0.201,length=50), # for cyan
seq(-0.20,0.20,length=25), # for black
seq(0.201,1.10,length=50), # for yellows
seq(1.101,2.0,length=50)) # for red
if (is.null(variableList))
{
variableList <- colnames(data)
names(variableList) <- colnames(data)
}
if (inherits(variableList,"data.frame"))
{
vnames <- as.vector(variableList[,1]);
}
else
{
vnames <- names(variableList[!(names(variableList) %in% Outcome)]);
}
if (length(vnames)>2)
{
frm <- paste(Outcome," ~ ",1);
added = 0;
for (i in 1:length(vnames))
{
if ((vnames[i] != "")&&(vnames[i] != "1"))
{
frm <- paste(frm,"+",vnames[i])
added = added + 1;
}
}
modelFrame <- model.frame(formula(frm),data);
rownames(modelFrame) <- rownames(data)
if (is.null(varRank))
{
topvarID <- seq(1, added, 1)
hits <- seq(1, added, 1)
}
else
{
topvarID <- as.numeric(rownames(varRank));
hits <- as.vector(as.matrix(varRank))
}
orderData <- cbind(data[,Outcome]);
orderData <- as.data.frame(orderData);
rownames(orderData) <- rownames(data);
colnames(orderData) <- c(Outcome);
cn = 1;
if (!is.null(prediction))
{
if (prediction[1]!=Outcome)
{
cn = cn + 1;
orderData <- cbind(orderData,as.vector(prediction));
colnames(orderData)[cn] <- "prediction";
}
}
for ( i in 1:length(topvarID))
{
if (hits[i]>0)
{
orderData <- cbind(orderData,modelFrame[,topvarID[i]+1])
cn = cn + 1;
colnames(orderData)[cn] <- colnames(modelFrame)[topvarID[i]+1];
}
}
dataMat <- as.matrix(orderData);
minout <- min(data[,Outcome]);
maxout <- max(data[,Outcome]);
if (!is.null(prediction))
{
if (prediction[1]!=Outcome)
{
if (transpose)
{
orderData <- eval(parse(text=paste("with(orderData,orderData[order(",Outcome,",orderData[,2]),])")))
}
else
{
orderData <- eval(parse(text=paste("with(orderData,orderData[order(-",Outcome,",-orderData[,2]),])")))
}
}
}
else
{
sortsum <- rowSums (abs(orderData), na.rm = TRUE)
orderData <- eval(parse(text=paste("with(orderData,orderData[order(-",Outcome,",-sortsum),])")))
cm <- as.data.frame(cor(t(orderData[,-1]),method="pearson"));
cm[is.na(cm)] <- -2;
# save(cm,file="cormat.RDATA");
rownames (cm) <- c(1:nrow(cm))
colnames (cm) <- c(1:nrow(cm))
indx <- 1
ix <- 1
rm <- cm;
thenames <- colnames(cm)
for (i in 1:(nrow(cm)-2))
{
rm <- cm[ix,-indx]
ix <- as.integer(names(which.max(rm)))
indx <- append(indx,ix)
}
indx <- append(indx,as.integer(thenames[-indx]))
# cat(indx,"\n")
if (transpose)
{
orderData <- orderData[rev(indx),];
orderData <- eval(parse(text=paste("with(orderData,orderData[order(",Outcome,"),])")))
}
else
{
orderData <- orderData[indx,];
orderData <- eval(parse(text=paste("with(orderData,orderData[order(-",Outcome,"),])")))
}
}
orderData <- as.matrix(orderData);
nr <- nrow(orderData);
index <- floor(nr*(orderData[,1]-minout)/(1.0001*maxout-minout))+1;
rowcolors <- colorRampPalette(outcomeColors)(n = nr)[index];
orderData <- orderData[,-1];
if (inherits(Scale,"logical"))
{
if (Scale) orderData <- scale(orderData);
}
nclass <- length(table(data[,Outcome]))
nticks <- min(5,nclass);
# print(nclass)
colorange = FALSE;
if (inherits(Scale,"logical"))
{
colorange = !Scale;
}
if (inherits(Scale,"numeric"))
{
colorange = TRUE;
}
if (colorange)
{
if (inherits(Scale,"numeric"))
{
kmin <- Scale[1];
kmax <- Scale[2];
}
else
{
kmin <- min(orderData);
kmax <- max(orderData);
}
brange <- (kmax-kmin)/5;
col_breaks = c(
seq(kmin,kmin+0.99*brange,length=45), # for blue
seq(kmin+brange,kmin+1.99*brange,length=45), # for cyan
seq(kmin+2*brange,kmin+2.99*brange,length=45), # for black
seq(kmin+3*brange,kmin+3.99*brange,length=45), # for yellows
seq(kmin+4*brange,kmax,length=45)) # for red
}
if (transpose)
{
if (is.null(prediction))
{
if (hCluster==TRUE)
{
heatMap <- gplots::heatmap.2(t(orderData),
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="both", # raw and column dendrogram
ColSideColors=rowcolors,
# lmat=rbind(c(0,5,0,4,0), c(0,3,1,2,0)),
# lhei=c(1.5,4.5),
# lwid=c(0.10,3,3,2,0.10),
...)
}
else
{
# rownames(orderData) <- format(orderData[,1],digits=4);
heatMap <- gplots::heatmap.2(t(orderData),
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
ColSideColors=rowcolors,
dendrogram=dendrogram,
Colv=Colv,
Rowv=Rowv,
...) # turn off row clustering
}
}
else
{
if (hCluster==FALSE)
{
heatMap <- gplots::heatmap.2(t(orderData),
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram=dendrogram,
Colv=Colv,
Rowv=Rowv,
ColSideColors=rowcolors,
...)
}
else
{
# rownames(orderData) <- format(orderData[,1],digits=4);
# index <- ((orderData[,1]+2)/4)
# index <- index*(index>0);
# index <- index*(index<1)+0.999*(index>=1);
# index <- floor(nrow(orderData)*index)+1;
# rowcolors <- colorRampPalette(theFiveColors)(n = nrow(orderData))[index];
# orderData <- orderData[,-1];
heatMap <- gplots::heatmap.2(t(orderData),
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="col", #
ColSideColors=rowcolors,
# lmat=rbind(c(0,5,0,4,0), c(0,3,1,2,0)),
# lhei=c(1.5,4.5),
# lwid=c(0.10,2,0.25,6.55,0.10),
Rowv="NA", # turn off col clustering
...)
}
}
rowcolors <- colorRampPalette(outcomeColors)(n = min(100,nclass));
color.bar(rowcolors,min=minout,max=maxout,nticks=nticks,Title=Outcome,omd=c(0.96,0.99,0.83,0.95))
}
else
{
if (is.null(prediction))
{
if (hCluster==TRUE)
{
heatMap <- gplots::heatmap.2(orderData,
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="both", # raw and column dendrogram
RowSideColors=rowcolors,
# lmat=rbind(c(0,5,0,4,0), c(0,3,1,2,0)),
# lhei=c(1.5,4.5),
# lwid=c(0.10,2,0.25,6.55,0.10),
...) # turn off column clustering
}
else
{
# rownames(orderData) <- format(orderData[,1],digits=4);
heatMap <- gplots::heatmap.2(orderData,
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram=dendrogram,
Colv=Colv,
Rowv=Rowv,
RowSideColors=rowcolors,
...)
}
}
else
{
if (hCluster==FALSE)
{
heatMap <- gplots::heatmap.2(orderData,
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram=dendrogram,
Colv=Colv,
Rowv=Rowv,
RowSideColors=rowcolors,
...)
}
else
{
# rownames(orderData) <- format(orderData[,1],digits=4);
# index <- ((orderData[,1]+2)/4)
# index <- index*(index>0);
# index <- index*(index<1)+0.999*(index>=1);
# index <- floor(nrow(orderData)*index)+1;
# rowcolors <- colorRampPalette(theFiveColors)(n = nrow(orderData))[index];
# orderData <- orderData[,-1];
heatMap <- gplots::heatmap.2(orderData,
main = title, # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins =c(7,8), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="row", #
RowSideColors=rowcolors,
# lmat=rbind(c(0,5,0,4,0), c(0,3,1,2,0)),
# lhei=c(1.5,4.5),
# lwid=c(0.10,2,0.25,6.55,0.10),
Colv="NA", # turn off col clustering
...)
}
}
rowcolors <- colorRampPalette(outcomeColors)(n = min(100,nclass));
color.bar(rowcolors,min=minout,max=maxout,nticks=nticks,Title=Outcome,omd=c(0.04,0.07,0.07,0.20))
}
par(opg)
result <- list(dataMatrix=dataMat,
orderMatrix=orderData,
heatMap=heatMap)
}
else
{
cat("No enough features for heatmap\n");
result <- NULL
}
# par(pty='m',mfrow=c(1,1))
return (result);
}
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