(This article was first published on

**rforcancer**, and kindly contributed to R-bloggers)I hope the code here is fairly self-explanatory with the inset annotations. I feel this is just a bit ‘prettier’ than heatmap.2 and has for me the right balance of options and extensibility. I have also found it difficult to produce high quality plots with heatmap.2- whereas ggplots especially with RStudio assistance in resizing PNG turn out better IMHO.

## m=matrix(data=sample(rnorm(100,mean=0,sd=2)), ncol=10) ## this function makes a graphically appealing heatmap (no dendrogram) using ggplot ## whilst it contains fewer options than gplots::heatmap.2 I prefer its style and flexibility ggheat=function(m, rescaling='none', clustering='none', labCol=T, labRow=T, border=FALSE, heatscale= c(low='blue',high='red')) { ## the function can be be viewed as a two step process ## 1. using the rehape package and other funcs the data is clustered, scaled, and reshaped ## using simple options or by a user supplied function ## 2. with the now resahped data the plot, the chosen labels and plot style are built require(reshape) require(ggplot2) ## you can either scale by row or column not both! ## if you wish to scale by both or use a differen scale method then simply supply a scale ## function instead NB scale is a base funct if(is.function(rescaling)) { m=rescaling(m) } else { if(rescaling=='column') m=scale(m, center=T) if(rescaling=='row') m=t(scale(t(m),center=T)) } ## I have supplied the default cluster and euclidean distance- and chose to cluster after scaling ## if you want a different distance/cluster method-- or to cluster and then scale ## then you can supply a custom function if(is.function(clustering)) { m=clustering(m) }else { if(clustering=='row') m=m[hclust(dist(m))$order, ] if(clustering=='column') m=m[,hclust(dist(t(m)))$order] if(clustering=='both') m=m[hclust(dist(m))$order ,hclust(dist(t(m)))$order] } ## this is just reshaping into a ggplot format matrix and making a ggplot layer rows=dim(m)[1] cols=dim(m)[2] melt.m=cbind(rowInd=rep(1:rows, times=cols), colInd=rep(1:cols, each=rows) ,melt(m)) g=ggplot(data=melt.m) ## add the heat tiles with or without a white border for clarity if(border==TRUE) g2=g+geom_rect(aes(xmin=colInd-1,xmax=colInd,ymin=rowInd-1,ymax=rowInd, fill=value),colour='white') if(border==FALSE) g2=g+geom_rect(aes(xmin=colInd-1,xmax=colInd,ymin=rowInd-1,ymax=rowInd, fill=value)) ## add axis labels either supplied or from the colnames rownames of the matrix if(labCol==T) g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=colnames(m)) if(labCol==F) g2=g2+scale_x_continuous(breaks=(1:cols)-0.5, labels=rep('',cols)) if(labRow==T) g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rownames(m)) if(labRow==F) g2=g2+scale_y_continuous(breaks=(1:rows)-0.5, labels=rep('',rows)) ## get rid of grey panel background and gridlines g2=g2+opts(panel.grid.minor=theme_line(colour=NA), panel.grid.major=theme_line(colour=NA), panel.background=theme_rect(fill=NA, colour=NA)) ## finally add the fill colour ramp of your choice (default is blue to red)-- and return return(g2+scale_fill_continuous("", heatscale[1], heatscale[2])) } ## NB because ggheat returns an ordinary ggplot you can add ggplot tweaks post-production e.g. ## data(mtcars) ## x= as.matrix(mtcars) ## ggheat(x, clustCol=T)+ opts(panel.background=theme_rect(fill='pink'))

Here is quick example with the same colourscheme as gplots::heatmap.2

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