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content/post/2008-04-05-performance-and-r.markdown

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+---
+author: einar
+categories:
+- Science
+comments: true
+date: "2008-04-05T13:12:18Z"
+header:
+  image_fullwidth: banner_other.jpg
+slug: performance-and-r
+tags:
+- bioinformatics
+- microarray
+- R
+- Science
+title: Performance and R
+disable_share: true
+wordpress_id: 390
+---
+
+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.
+
+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 ?
+
+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).
+  *[SAM]: Significance Analysis of Microarrays