Ok, so you can just look at the code by typing the name of the function at the R prompt, or use edit(pheatmap) to see it in your default editor.
Around line 14 and 23, you'll see that another function is called for computing the distance matrices (for rows and columns), given a distance function (R dist) and a method (compatible with hclust for hierarchical clustering in R). What does this function do? Use getAnywhere("cluster_mat") to print it on screen, and you soon notice that it does nothing more than returning an hclust object, that is your dendrogram computed from the specified distance and linkage options.
So, if you already have your distance matrix, change line 14 (rows) or 23 (columns) so that it reads, e.g.
tree_row = hclust(my.dist.mat, method="complete")
where my.dist.mat is your own distance function, and complete is one of the many methods available in hclust (see help(hclust)). Here, it is important to use fix(pheatmap) and not edit(pheatmap); otherwise, the edited function will not be callable in the correct environment/namespace.
This is a quick and dirty hack that I would not recommend with larger package. It seems to work for me at least, that is I can use a custom distance matrix with complete linkage for the rows.
In sum, assuming your distance matrix is stored in a variable named dd,
library(pheatmap)
fix(pheatmap)
# 1. change the function as you see fit
# 2. save and go back to R
# 3. if your custom distance matrix was simply read as a matrix, make sure
# it is read as a distance matrix
my.dist.map <- dd # or as.dist(dd)
Then, you can call pheatmap as you did but now it will use the results of hclust applied to my.dist.map with complete linkage. Please note that you just have to ensure that cluster_rows=TRUE (which is the default). Now, you may be able to change
- the linkage method
- choose between rows or columns
by editing the package function appropriately.
Try the pheatmap package. The link includes example code and images for clustering and displaying genetic data. It can apply a variety of clustering methods to your data before displaying them.
You should be able to get started with just
library(pheatmap)
pheatmap(mat)
and then you'll have lots of options for adjusting the display and clustering parameters if the defaults don't suit you.
Best Answer
I don't know a specific function for that. The ones I used generally take raw data or a distance matrix. However, it would not be very difficult to hack already existing code, without knowing more than basic R. Look at the source code for the
cim()function in the mixOmics package for example (I choose this one because source code is very easy to read; you will find other functions on the Bioconductor project). The interesting parts of the code are l. 92-113, where they assign the result of HC toddc, and around l. 193-246 where they devised the plotting regions (you should input the values of your distance matrix in place of mat when they callimage()). HTHEdit
A recent Google search on a related subject lead me to
dendrogramGrob()from the latticeExtra package. Assuming you already have your sorted dendrogram object, you can skip the first lines of the example code from the on-line help and get something like this (here, with themtcarsdataset):