Recently I had to annotate a large (10,000+) number of genes identified by Entrez Gene IDs. My goal was to avoid “annotation files” (basically CSV files) that a part of wet lab group likes, because I wanted to stay up-to-date without having to remember to update them. So the obvious solution was to use a service available on the web, and in an automated way. For reference, I just tried to attach gene symbol, gene name, chromosome and cytoband.

I’m often wondering why people only resort to R when working with microarrays. I can understand that Bioconductor offers a plethora of different packages and that R’s statistical functions come in handy for many applications, but still, I think people underestimate the impact of performance. R is not a performing language at all, it doesn’t parallelize well when using HPC (at least from the talks I’ve had with people studying the matter), and in general is a memory and resource hog.

Fourteen days since my last post. Quite a while, indeed. Mostly I’ve been stumbled with work and some health related issues. Anyway, I thought I’d follow up on the meta analysis matter I discussed in my last post. It turns out that it’s a fault of both limma and the data sets, because apparently the raw data found in the Stanford Microarray Database have different length, gene-wise (a result of not all spots on the array being good?

Again in the past days I’ve been banging my head thanks to the fact that doing meta-analysis with microarray data is more difficult than what it seems. The problem sometimes lies in the data, sometimes lies in the analysis software and sometimes in a combination of factors. When doing work on a public data set (Zhao et al., 2005), I had to start analysis from raw data. Now, I tried using both the limma and marray Bioconductor packages, but both of them bail out with cryptic error messages.

While working today on an annotation class in Python I stumbled on a problem. Normally I work with lists of genes that are consistent, i.e. all Entrez Gene IDs (or RefSeq IDs, or Genome Browser IDs…), but today I had a list of mixed identifiers. The subsequent idea was “let’s implement auto-detection of common identifiers in the class”. The problem is… is there any actual documentation on how identifiers are made?

Today I was looking for an easy way to do some calculations of raw expression data on Affymetrix arrays, but I didn’t want to use R: I have already mentioned how I don’t like its design and implementation. While looking for some documentation, I stumbled upon this nifty little program called RMAExpress.