heatmap (2): heatmap.2()

Abstract: The function heatmap.2() in the R package ‘gplots’ provides a number of extensions to the default R function heatmap().

Here is the options in heatmap.2().

heatmap.2 (x,

# dendrogram control

Rowv = TRUE,

Colv=if(symm)"Rowv" else TRUE,

distfun = dist,

hclustfun = hclust,

dendrogram = c("both","row","column","none"),

reorderfun = function(d, w) reorder(d, w),

symm = FALSE,

# data scaling

scale = c("none","row", "column"),

na.rm=TRUE,

# image plot

revC = identical(Colv, "Rowv"),

add.expr,

# mapping data to colors

breaks,

symbreaks=any(x < 0, na.rm=TRUE) || scale!="none",

# colors

col="heat.colors",

# block sepration

colsep,

rowsep,

sepcolor="white",

sepwidth=c(0.05,0.05),

# cell labeling

cellnote,

notecex=1.0,

notecol="cyan",

na.color=par("bg"),

# level trace

trace=c("column","row","both","none"),

tracecol="cyan",

hline=median(breaks),

vline=median(breaks),

linecol=tracecol,

# Row/Column Labeling

margins = c(5, 5),

ColSideColors,

RowSideColors,

cexRow = 0.2 + 1/log10(nr),

cexCol = 0.2 + 1/log10(nc),

labRow = NULL,

labCol = NULL,

srtRow = NULL,

srtCol = NULL,

adjRow = c(0,NA),

adjCol = c(NA,0),

offsetRow = 0.5,

offsetCol = 0.5,

colRow = NULL,

colCol = NULL,

# color key + density info

key = TRUE,

keysize = 1.5,

density.info=c("histogram","density","none"),

denscol=tracecol,

symkey = any(x < 0, na.rm=TRUE) || symbreaks,

densadj = 0.25,

key.title = NULL,

key.xlab = NULL,

key.ylab = NULL,

key.xtickfun = NULL,

key.ytickfun = NULL,

key.par=list(),

# plot labels

main = NULL,

xlab = NULL,

ylab = NULL,

# plot layout

lmat = NULL,

lhei = NULL,

lwid = NULL,

# extras

extrafun=NULL,

...

)

Here is the default output by heatmap.2(). The colorful bar is added in heatmap.2().

> library(gplots)

> x<-as.matrix(scale(mtcars))

> #the dafault output

> heatmap.2(x)

#the color palette

my_palette <- colorRampPalette(c("yellow", "white", 'red'))(n = 100)

heatmap.2(x, col=my_palette)

#z scores instead of values

>heatmap.2(x, col=my_palette, scale='row')

#turn off trace

>heatmap.2(x, col=my_palette, scale='row', trace='none')

#other clustering methods

>dist.pear <- function(x) as.dist(1-cor(t(x)))

>hclust.ave <- function(x) hclust(x, method="average")

>heatmap.2(x, col=my_palette, scale='row', trace='none', distfun=dist.pear, hclust=hclust.ave)

The heatmap by groups using the options ColSideColors or RowSideColors.

#heatmap by group

>library("ALL")

>data("ALL")

>eset <- ALL[, ALL$mol.biol %in% c("BCR/ABL", "ALL1/AF4")]

>f <- factor(as.character(eset$mol.biol))

>design <- model.matrix(~f)

>fit <- eBayes(lmFit(eset,design))

>selected <- p.adjust(fit$p.value[, 2]) <0.05

>esetSel <- eset [selected, ]

>color.map <- function(mol.biol) { if (mol.biol=="ALL1/AF4") "blue" else "red" }

>patientcolors <- unlist(lapply(esetSel$mol.bio, color.map))

#

>heatmap.2(exprs(esetSel), col=my_palette, ColSideColors=patientcolors, trace='none')

writing date: 20150122