I recently started using RStudio, the amazing new IDE for R. You can view all of RStudio's keyboard shortcuts by going to the help menu, but I made this printable reference for myself and thought I'd share it. I only included the Windows shortcuts, and I cut out all the obvious ones (Ctrl-S for save, Ctrl-O for open, etc) so it would fit neatly on one page.
The *ABEL suite of R packages and software for genetic analysis has grown substantially since the appearance of GenABEL and the previously mentioned ProbABEL R packages. There are now a handful of useful R packages and other software utilities facilitating genome-wide association studies, analysis of imputed data, meta-analysis, efficient data storage, prediction, parallelization, and mixed model methods.
Just saw the announcement of the availability of Rstudio, a new (free & open source) integrated development environment for R that works on Windows, Mac, and Linux. Judging from the screenshots, it looks like Rstudio supports syntax highlighting for Sweave &
About a year ago I wrote a post about Dropbox - a free, awesome, cross-platform utility that syncs files across multiple computers and securely backs up your files online. Dropbox is indispensable in my own workflow. I store all my R code, perl scripts, and working manuscripts in my Dropbox. You can also share folders on your computer with other Dropbox users, which makes coauthoring a paper and sharing manuscript files a trivial task.
At the request of a commenter I just wanted to clarify that any code released here for R or anything else is free and open source unless specifically stated otherwise. The open source (GPLv2) license for any code on GGD can be found on this page.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.
A few weeks ago I covered an R package for efficient mixed model regression that is capable of simultaneously accounting for both population stratification and relatedness to compute unbiased estimates of standard errors and p-values for genetic association studies.
I recently did an analysis for the eMERGE network where I had lots of individuals from a small town in central Wisconsin where many of the subjects were related to one another. The subjects could not be treated as independent, but I could not use a family-based design either. I ended up using a mixed model approach using previously mentioned GenABEL.
I've been using GenABEL for some time now for GWAS analysis using related individuals. It has an excellent set of functions for estimating a kinship matrix from a dense marker panel and then using this in a linear mixed effects model to allow for related individuals in the analysis of a quantitative trait.
Suppose you have billions of reads from a hot new sequencing machine and you want to simultaneously align these reads and call SNPs very quickly on the cheap. Check out an open source tool called Crossbow and the recent paper in Genome Biology.
Doug Robinson (JMP Academic Division, SAS Institute) is giving a presentation of the JMP Genomics software from SAS illustrating data analysis for genetics, expression, and copy number variation studies. Agenda includes: CNV analysis, merging CNV data sets of continuous traits, and QC, analysis, and downstream applications of expression studies.