A few weeks ago I suddenly reached the point that every graduate student once thought would never come - time to start writing my thesis. With a blank page and a blinking cursor staring me in the face it's time to compile all of my published and unpublished work I've accumulated over the last few years and wordsmith this pile of papers and results into a single cohesive unit.

Last Year I introduced you to R Commander, a nice graphical user interface (GUI) for R for those of you who are still hesitant to leave the clicky-box style research a la SPSS for the far more superior reproducible research using R. As most of you know I'm a huge fan of ggplot2. Many of you came to the short course Hadley Wickham gave here a few weeks ago on ggplot2 and plyr.

Last year I linked to a series of perspectives in NEJM with contrasting views on the success or failure of GWAS - David Goldstein's paper and Nick Wade's synopsis that soon followed in the New York Times being particularly pessimistic. Earlier this year I was swayed by an essay in Cell by Jon McClellan and Mary-Claire King condemning the common disease common variant hypothesis and chalking up most GWAS hits to population stratification.

Lucila Ohno-Machado, Professor of Medicine and Chief of the Division of Biomedical Informatics at UC-San Diego, will be giving a talk on "Accuracy of Individualized Risk Estimates for Personalized Medicine" next week, August 18, noon-1pm in 202 Light Hall. This should be an interesting perspective from a scientist with medical training on the utility of personal genomics tools in making healthcare decisions.

Aviad Klein over at My ContRibution wrote a convenient R function to list the classes of all the vectors that make up a data.frame. You would think apply(kyphosis,2,class) would do the job but it doesn't - it calls every vector a character class. Aviad wrote an elegant little function that does the job perfectly without having to load any external package:

Last week Will showed you a bash script version of a sed command covered here a while back that would convert PLINK output from the default variable space-delimited format to a more database-loading-friendly tab or comma delimited file. A commenter asked how to do this on windows, so I'll share the way I do this using a perl script which you can use on windows after installing ActivePerl.

Hadley Wickham, creator of ggplot2, an immensely popular framework for Tufte-friendly data visualization using R, is teaching two short courses at Vanderbilt this week. Once we opened registration to Vanderbilt students and staff we instantly filled all the available seats, so unfortunately I wasn't able to announce the course here. But the good news is that Hadley's made all the data, code, and slides from the course available online here.

A live webcast of the House Committee on Energy and Commerce hearing on “Direct-to-Consumer Genetic Testing and the Consequences to the Public Health" is available at this link. I had trouble viewing the webcast in firefox - had to save the link and open it with VLC media player to get it working. You can also follow the #HouseDTC hastag on Twitter.

Jeff Barret (@jcbarret on Twitter) over at Genomes Unzipped (@GenomesUnzipped) has posted a nice guide for the uninitiated on how to read a GWAS paper. Barret outlines five critical areas that readers should pay attention to: sample size, quality control, confounding (including population substructure), the replication requirement, and biological significance.

You can find two nice recaps of last week's personalized medicine policy forum on Genomics Law Report and 23andMe's blog, The Spittoon.

For those of you not following GGD on Twitter you may not have seen this - California State Senator Alex Padilla and 23andMe are hosting a policy forum entitled "Genomics and the Consumer: The Present and Future of Personalized Medicine" today in San Francisco.