Luca Beltrame
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26 lines
1.7 KiB
Markdown
26 lines
1.7 KiB
Markdown
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author: einar
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categories:
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- Science
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comments: true
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date: "2008-04-05T13:12:18Z"
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header:
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image_fullwidth: banner_other.jpg
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slug: performance-and-r
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tags:
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- bioinformatics
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- microarray
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- R
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- Science
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title: Performance and R
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omit_header_text: true
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disable_share: true
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wordpress_id: 390
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---
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I'm often wondering why people only resort to R when working with microarrays. I can understand that [Bioconductor](http://www.bioconductor.org) 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.
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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](http://rmaexpress.bmbolstad.com/) (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 ?
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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](http://www.ncbi.nlm.nih.gov/pubmed/16824223?ordinalpos=4&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum) comes to mind).
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*[SAM]: Significance Analysis of Microarrays
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