About a year ago I wrote a post about producing scatterplot matrices in R. These are handy for quickly getting a sense of the correlations that exist in your data. Recently someone asked me to pull out some relevant statistics (correlation coefficient and p-value) into tabular format to publish beside a scatterplot matrix.

The 20th annual ISMB meeting was held over the last week in Long Beach, CA. It was an incredible meeting with lots of interesting and relevant talks, and lots of folks were tweeting the conference, usually with at least a few people in each concurrent session. I wrote the code below that uses the twitteR package to pull all the tweets about the meeting under the #ISMB hashtag. You can download that raw data here.

I saw this plot in the supplement of a recent paper comparing microarray results to RNA-seq results. Nothing earth-shattering in the paper - you've probably seen a similar comparison many times before - but I liked how they solved the overplotting problem using heat-colored contour lines to indicate density. I asked how to reproduce this figure using R on Stack Exchange, and my question was quickly answered by Christophe Lalanne.

A few folks have asked me how I get my news and stay on top of what's going on in my field, so I thought I'd share my strategy.

I'm a huge supporter of the Free and Open Source Software movement. I've written more about R than anything else on this blog, all the code I post here is free and open-source, and a while back I invited you to steal this blog under a cc-by-sa license. Every now and then, however, something comes along that just might be worth paying for.

If you're doing any kind of big data analysis - genomics, transcriptomics, proteomics, bioinformatics - then unless you've been on vacation the last few weeks you've no doubt heard about the NSF/NIH BIGDATA  Initiative (here's the NSF solicitation and here's the New York Times article about the funding opportunity). The solicitation "aims to advance core scientific and technological means of managing, analyzing, visualizing, and

There were lots of interesting developments this month that didn't work their way into a full blog post. Here is an incomplete list of what I've been tweeting about over the last few weeks.

I get asked frequently how to convert from one gene identifier to another. This can be tricky, especially when relying on gene symbols, as Will pointed out in a previous post a few years ago. There are several tools that can do this, including DAVID and the previously mentioned new Biomart ID Converter, but I still prefer using the Ensembl Biomart for this because of its added flexibility and annotation.

*Edit March 12* Be sure to look at the comments, especially the commentary on Hacker News - you can supercharge the find|xargs idea by using find|parallel instead. --- Do you ever discover a trick to do something better, faster, or easier, and wish you could reclaim all the wasted time and effort before your discovery?

I get a lot of requests in the core about running a "pathway analysis." Someone ran a handful of gene expression arrays, or better yet, ran an RNA-seq experiment (with replicates!). These, and many other kinds of high-throughput assays (GWAS, ChIP-seq, etc.) result in a list of genes and some associated p-value, fold change, or other statistic. Here's some R code to download public data from a study on susceptibility to colorectal cancer.