One of R's biggest strengths is its unparalleled graphing capabilities.  Just see any of our previous posts on ggplot2, visualization, or other posts tagged with R. R has several fundamentally different systems for plotting, including base graphics, lattice, and ggplot2.  Furthermore, many add-on packages come with their own functions for producing problem-domain specific graphics.

Following up on last week's coverage of the Genotyping Portal, check out this new review article on next-generation sequencing in Nature Reviews Genetics.  One major focus of this paper is that the next generation of sequencing platforms each use fundamentally different technologies.

Diego Forero has compiled a comprehensive list of primary publications on commonly used SNP genotyping and DNA sequencing technologies (including SNP arrays, Sequenom, TaqMan, Pyrosequencing, Molecular Beacons, FP-TDI, Invader, xMAP, SNaPshot, SNPlex, Sanger, 454, Illumina, Helicos, SOLiD, Complete Genomics, Bisulfite sequencing, and others).

Do you use SSH to connect to a remote Linux machine from your local Windows computer?  Ever needed to run a program on that Linux machine that displays graphical output, or uses a GUI? I was in this position last week trying to make figures using ggplot2 in R of results from an analysis of GWAS data which required using a 64-bit Linux machine with more RAM than my 32-bit windows machine can see.

A Beautiful WWW put together a great set of resources for getting started with machine learning in R.  First, they recommend the previously mentioned free book, The Elements of Statistical Learning.  Then there's a link to a list of dozens of machine learning and statistical learning packages for R.  Next, you'll need data.  Hundreds of free real datasets are available at the UCI machine learning repository.

The New York Times had an interesting piece yesterday about how SAS is facing several business threats from companies like the recently IBM-acquired SPSS, and from burgeoning interest in open-source software like R.

The 2009 Cancer Epidemiology, Biostatistics, and Bioinformatics Retreat will be held on Friday, December 4th, 2009, from 1:30 pm to 5:00 pm, on the eighth floor of the VICC building (898B PRB). The purpose of the retreat is to promote interactions among biostatisticians, bioinformaticians, epidemiologists, clinical investigators, and other translational researchers.

Theresa Scott, instructor of the previously mentioned R workshop and weekly R clinic, is giving a lecture entitled "Reproducible Research with R, LaTeX, & Sweave" in MRB III, room 1220, this Wednesday 11/18 at 1:30.  You can see more details about the lecture here. Looks like her slides as well as much more introductory material on R, Latex, and Sweave are on her website. Reproducible Research with R, LaTeX, &

Way back will wrote on this topic.  See his previous post for Stata code for doing this.  Unfortunately the R package that was used to create QQ-plots here has been removed from CRAN, so I wrote my own using ggplot2 and some code I received from Daniel Shriner at NHGRI. Of course you can use R's built-in qqplot() function, but I could never figure out a way to add the diagonal using base graphics.

While flirting around with previously mentioned ggplot2 I came across an incredibly useful set of functions in the plyr package, made by Hadley Wickham, the same guy behind ggplot2.  If you've ever used MySQL before, think of "GROUP BY", but here you can arbitrarily apply any R function to splits of the data, or write one yourself.

There are no common disorders - only extremes of quantitative traits. --- That's the argument made by Plomin, Haworth, and Davis in a great review paper just published online in Nature Reviews Genetics.