This looks interesting. Department of Biostatistics Seminar/Workshop Series: A Multivariate Methodology for Analyzing Genome-wide Association Studies, by Janice Brodsky, PhD, UCLA.

GGD is now on Twitter! I'll be linking to all of our posts on the Twitter page, and occasionally post something there that may not make its way into a full length post here on the blog. You can follow us on Twitter here @genetics_blog.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

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, &