25 lines
1.6 KiB
Markdown
25 lines
1.6 KiB
Markdown
---
|
|
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
|