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. For example, it takes much more to perform RMA via R that with RMAExpress (which is a C++ application): the latter works also better with regards to memory utilization. I can understand the complexity of some statistical procedures, but what about ?
The surprising aspect is that aside by a few exceptions (like the aforementioned RMAExpress) no one has tried to write more performing implementations of certain algorithms. I for one would welcome a non-R implementation of SAM (the original implementation works in Excel… ugh) or similar algorithms. Otherwise we would be stuck with programs that are interesting, but way too memory hungry (AMDA comes to mind).
*[SAM]: Significance Analysis of Microarrays
Luca Beltrame SCIENCE
bioinformatics microarray R Science