Meta analysis difficulty increasing

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. From what I’ve learnt by googling around, it seems that R doesn’t like batch loading of tables of different length.

I have 177 samples and I have to normalize them all together. Apparently this is a quirk of marray and limma (or worse, R itself) which is preventing me to work properly. And this is not the first time it happens, either: in the past year I’ve lost a lot of time dealing with software issues rather than performing real analsis. The problem has been posted already on some R mailing lists (and on BioC, too), but judging from the responses I doubt I’ll see a solution.

I guess I’ll have to work around this somehow (and of course, this doesn’t improve the idea I have of R…).

meta-analysis microarray Science

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