Tag Archives: bioinformatics

Data clustering with Python

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Notice:Just now I realized this has been linked to to a Stack Overflow question. I recently wrote a new post that uses a different technique and a combination of R and Python. Check it out!

Following up my recent post, I’ve been looking for alternatives to TMeV. So far I’ve found the R package pvclust and the Pycluster library, part of BioPython. The first one also performs bootstrapping (I’m not sure if it’s similar to what support trees do, but it’s still better than no resampling at all). I’ve found another Python project but it is still too basic to perform what I need.

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Buggy bioinformatics software

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As people who read my science-related posts know already, I’m not a big fan of {{post id=”software-and-biological-research” text=”software made just to support a publication”}}. Recently I’ve stumbled again into similar software. Namely, I’m talking about the TIGR Multiexperiment Viewer (TMeV), a Java-based program which is often used for microarray analysis. It’s not exactly “fit for publication”, because it has reached version 4 last year, but shows some of the problems ({{post id=”genbugg” text=”mentioned already”}}) with releasing bioinformatics software.

I use TMeV mostly because I didn’t find any other implementation of the hierarchical clustering algorithm with support trees. However, I’ve stumbled upon a very annoying bug in the most recent version. Normally I use average linkage clustering and as the distance metric I employ the Pearson’s correlation, and with gene and sample bootstrapping: with certain files this makes TMeV report errors at random during the iterations.

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SOFT file woes

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Today I started working on a data set published on GEO. As the sample data were somehow inconsistent (they mentioned 23 controls when I found 28), I decided to parse the SOFT file from GEO in order to get the exact sample information.

I did a grave mistake. First of all, Biopython‘s SOFT parser is horribly broken (doesn’t work at all) and quite undocumented: I could work around the lack of documentation (API docs) but not with the fact that it wouldn’t work. So I turned to R, which offers a GEO query module through Bioconductor.

Again that proved to be a terrible mistake. For a file containing 183 samples, the analysis is going on since four hours and with no sign of completing anytime soon (not to mention a  possible memory leak). After this, I gave up. I’m going to get the reduced data sheet and write a small parser in Python myself.

What is frustrating is the lack of quality: I could concentrate on my own work rather than reinventing the wheel for the nth time if the existing implementations worked. What’s the point in releasing non-working software? I could understand bugs, but this is one step further.