How to expand the dendogram in heatmap.2

Question

I have the following heatmap with the dendrogram like this. The complete data are here. The problem is that the dendrogram on the left is squished. How can I unsquish (expand) it without altering the column size of the heatmap? It is generated with this code: ``` #!/usr/bin/Rscript library(gplots); library(RColorBrewer); plot_hclust <- function(inputfile,clust.height,type.order=c(),row.margins=70) { # Read data dat.bcd <- read.table(inputfile,na.strings=NA, sep=" ",header=TRUE); rownames(dat.bcd) <- do.call(paste,c(dat.bcd[c("Probes","Gene.symbol")],sep=" ")) dat.bcd <- dat.bcd[,!names(dat.bcd) %in% c("Probes","Gene.symbol")] dat.bcd <- dat.bcd # Clustering and distance function hclustfunc <- function(x) hclust(x, method="complete") distfunc <- function(x) dist(x,method="maximum") # Select based on FC, as long as any of them >= anylim anylim <- 2.0 dat.bcd <- dat.bcd[ apply(dat.bcd, 1,function(x) any (x >= anylim)), ] # Clustering functions height <- clust.height; # Define output fil…

Accepted Answer

In your case the data has long tail, which is expected for gene expression data (lognormal).

```
data <- read.table(file='http://pastebin.com/raw.php?i=ZaGkPTGm', 
                   header=TRUE, row.names=1)

mat <- as.matrix(data[,-1]) # -1 removes the first column containing gene symbols
```

As you can see from the quantile distribution that the genes with the highest expression extend the range from 1.5 to above 300.

```
quantile(mat)

#     0%     25%     50%     75%    100% 
#  0.000   0.769   1.079   1.544 346.230 
```

When the hierarchical clustering is performed on unscaled data the resulting dendrogram may show bias towards the values with the highest expression, as seen in your example. This merits either a logarithmic or z-score transformation, among many (reference). Your dataset contains `values == 0`, which is a problem for log-transformation since `log(0)` is undefined.

Z-score transformation (reference) is implemented within `heatmap.2`, but it's important to note that the function computes the distance matrix and runs clustering algorithm before scaling the data. Hence the option `scale='row'` doesn't influence the clustering results, see my earlier post (differences in heatmap/clustering defaults in R) for more details.

I would propose that you scale your data before running `heatmap.2`:

```
# scale function transforms columns by default hence the need for transposition.
z <- t(scale(t(mat)))

quantile(z)

#         0%        25%        50%        75%       100% 
# -2.1843994 -0.6646909 -0.2239677  0.3440102  2.2640027

# set custom distance and clustering functions
hclustfunc <- function(x) hclust(x, method="complete")
distfunc <- function(x) dist(x,method="maximum")

# obtain the clusters
fit <- hclustfunc(distfunc(z))
clusters <- cutree(fit, 5)

# require(gplots)
pdf(file='heatmap.pdf', height=50, width=10)
heatmap.2(z, trace='none', dendrogram='row', Colv=F, scale='none', 
             hclust=hclustfunc, distfun=distfunc, col=greenred(256), symbreak=T,
             margins=c(10,20), keysize=0.5, labRow=data$Gene.symbol,
             lwid=c(1,0.05,1), lhei=c(0.03,1), lmat=rbind(c(5,0,4),c(3,1,2)),
             RowSideColors=as.character(clusters))
dev.off()
```

Also, see the additional posts here and here, which explain how to set the layout of the heatmap via `lmat`, `lwid` and `lhei` parameters.

The resulting heatmap is shown below (row and column labels are omitted):

How to expand the dendogram in heatmap.2

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