Nice is a relative term, what you probably mean that you want to customize it in a way that is not immediately available with the existing heatmap.

As it has been already mentioned by Michael in a comment (I'll add it here since I'd consider that an answer as well) you might want to consider alternative plotting libraries such as:

• ggplot2
• heatmap.plus
• heatmap.2

Biostar has the answer here:

First option is also my recommendation: gitools. since it is written in java you may use it on your linux machine without problems I hope.

Here is an awesome function using ggplot to generate a heatmap(ggheat) and producing visually appealing heatmaps. Available at this post: http://rforcancer.drupalgardens.com/content/ggheat-ggplot2-style-heatmap-function

 ## 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 reshaped 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 different 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

   numrows=dim(m)[1]
   numcols=dim(m)[2]
   melt.m=cbind(rowInd=rep(1:numrows, times=numcols), colInd=rep(1:numcols, each=numrows) ,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(length(labCol)==numcols)
   {
     g2=g2+scale_x_continuous(breaks=(1:numcols)-0.5, labels=labCol)
   }else
   {
     if(labCol==T) 
       g2=g2+scale_x_continuous(breaks=(1:numcols)-0.5, labels=colnames(m))
     if(labCol==F) 
       g2=g2+scale_x_continuous(breaks=(1:numcols)-0.5, labels=rep('',numcols))
   }

   if(length(labRow)==numrows)
   {
     g2=g2+scale_y_continuous(breaks=(1:numrows)-0.5, labels=labRow)
   }else
   {
     if(labRow==T)
       g2=g2+scale_y_continuous(breaks=(1:numrows)-0.5, labels=rownames(m))    
       if(labRow==F) 
       g2=g2+scale_y_continuous(breaks=(1:numrows)-0.5, labels=rep('',numrows))    
   }
   ## 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]))

 }

Usage:(ripped from the same page)

data(mtcars)
x=as.matrix(mtcars)
ggheat(x, clustering='column', rescaling='row', heatscale=c(low='red', high='yellow'))

I have found the following web app / source code to be a great way to visualize data as a heatmap. It is customizable enough for my needs while having almost no learning curve. Platform independant as well as it is written in C.

http://www.bioinformatics.ubc.ca/matrix2png/index.html

Update: If you are not opposed to doing a little programming, the python graphing class matlibplot has a nice heatmap output. An example with code can be found here: http://stackoverflow.com/questions/2369492/generate-a-heatmap-in-matplotlib-using-a-scatter-data-set