Posts Tagged ‘ Bioinformatics ’

A new twist on the identifier mapping problem

January 11, 2010
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A new twist on the identifier mapping problem

Yesterday, Deepak wrote about BridgeDB, a software package to deal with the “identifier mapping problem”. Put simply, biologists can name a biological entity in any way that they like, leading to multiple names for the same object. Easily solved, you might think, by choosing one identifier and sticking to it, but that’s apparently way too

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Samples per series/dataset in the NCBI GEO database

January 7, 2010
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Samples per series/dataset in the NCBI GEO database

Andrew asks: I want to get an NCBI GEO report showing the number of samples per series or data set. Short of downloading all of GEO, anyone know how to do this? Is there a table of just metadata hidden somewhere? At work, we joke that GEO is the only database where data goes in,

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RSRuby in the IRB console

August 6, 2009
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RSRuby in the IRB console

R is terrific, of course, for all your statistical needs. But those data structures! “Everything is a list.” Leading to such wondrous ways to access variables as “p <- Meta(gds)$platform", or "last <- mylist]])]". Sometimes, you want something more familiar. An array, a hash, a hash of arrays. Or, you may

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Select operations on R data frames

July 26, 2009
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Select operations on R data frames

The R language is weird - particularly for those coming from a typical programmer's background, which likely includes OO languages in the curly-brace family and relational databases using SQL. A key data structure in R, the data.frame, is used somethin...

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R String processing

July 2, 2009
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R String processing

Here's a little vignette of data munging using the regular expression facilities of R (aka the R-project for statistical computing). Let's say I have a vector of strings that looks like this:> coords "chromosome+:157470-158370" "chromosome+:1583...

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An R Wiki

April 21, 2008
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An R Wiki

It’s been ages since I visited the R website, so I don’t know how long they’ve had a wiki. It’s built using DokuWiki, one of my personal favourites. This is a great leap forward for R documentation, which is somewhat notorious for being (a) difficult to find and (b) difficult to understand when you find

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