hierarchical clustering with gene expression matrix in python

how can I do a hierarchical clustering (in this case for gene expression data) in Python in a way that shows the matrix of gene expression values along with the dendrogram? What I mean is like the example here:

http://www.mathworks.cn/access/helpdesk/help/toolbox/bioinfo/ug/a1060813239b1.html

shown after bullet point 6 (Figure 1), where the dendrogram is plotted to the left of the gene expression matrix, where the rows have been reordered to reflect the clustering.

How can I do this in Python using numpy/scipy or other tools? Also, is it computationally practical to do this with a matrix of about 11,000 genes, using euclidean distance as a metric?

EDIT: Many have suggested clustering packages, but I am still unsure how to plot the kind of im开发者_C百科age I linked to above in Python. How can I overlay a dendrogram alongside a heatmap matrix, using Matplotlib for example?

thanks.

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Many clustering methods including scipy.cluster start by sorting all pairwise distances, ~ 60 million in your case, not too big.
How long does the following take for you ?

import scipy.cluster.hierarchy as hier
import pylab as pl

def fcluster( pts, ncluster, method="average", criterion="maxclust" ):
    """ -> (pts, Y pdist, Z linkage, T fcluster, clusterlists)   
        ncluster = n1 + n2 + ... (including n1 singletons)
        av cluster size = len(pts) / ncluster
    """
    pts = np.asarray(pts)
    Y = scipy.spatial.distance.pdist( pts )  # ~ N^2 / 2
    Z = hier.linkage( Y, method )  # N-1                         
    T = hier.fcluster( Z, ncluster, criterion=criterion )
        # clusters = clusterlists(T)
    return (pts, Y, Z, T)

hier.dendrogram( Z )

How to permute the matrix and plot nicely was asked here in So in March, with a partial answer.

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You can do this with scipy's cluster.hierarchy module. The commands are actually even very similar. However, you will have to use correlation instead of corr as a parameter to pdist and rather than cluster the name of the function scipy's cluster module is fcluster. Also, for the dendrogram, the function is dendrogram in scipy as opposed to clustergram in Matlab.

You can definitely use a euclidean metric (think it is the default for pdist). I think that it should be feasible to do this with 11,000 genes because that will be 11000*(11000-1)/2 = 60494500 (11000 choose 2) distances to be computed. That's a large number, but certainly feasible I would think.

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A couple of people have made some decent progress in creating a prototype module for hierarchical clustering and heatmap visualization using scipy and matplotlib:

How to get flat clustering corresponding to color clusters in the dendrogram created by scipy

I've been adapting this code to make a full-fledged hierarchical clustering module that I can integrate into one of my transcriptome analysis packages. I'm pretty happy with the final product which will produce a heatmap using various clustering metrics and methods and coloring gradients. The code and an example output is shown here:

http://altanalyze.blogspot.com/2012/06/hierarchical-clustering-heatmaps-in.html