I wanted to write this earier, but I couldn’t: I’m now in a hotel in Maastricht, Netherlands, and waiting to get back tomorrow. I’ve been attending the 4th NuGO hands-on advanced microarray data analysis course and I even wanted to blog about it… but the hotel’s connection did not resolve any non-European web page until late today.

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.

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.