Many of you may be familiar with WebGestalt, a wonderful web utility developed by Bing Zhang at Vanderbilt for doing basic gene-set enrichment analyses. Last year, we invited Bing to speak at our annual retreat for the Vanderbilt Graduate Program in Human Genetics, and he did not disappoint! Bing walked us through his new tool called NetGestalt.
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.
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.
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.
Title: A Backstage Tour of ggplot2 with Hadley Wickham Date: Wednesday, February 8, 2012 Time: 11:00AM - 12:00PM Pacific Presenter: Hadley Wickham, Professor of Statistics, Rice University Register here.
In general, the standard practice for correcting for population stratification in genetic studies is to use principal components analysis (PCA) to categorize samples along different ethnic axes . Price et al. published on this in 2006, and since then PCA plots are a common component of many published GWAS studies.
I just discovered a handy function in R to produce a scatterplot matrix of selected variables in a dataset. The base graphics function is pairs(). Producing these plots can be helpful in exploring your data, especially using the second method below. Try it out on the built in iris dataset.
**** UPDATE, May 15 2014 ***** The functions described here have now been wrapped into an R package. View the updated blog post or see the online package vignette for how to install and use. If you'd still like to use the old code described here, you can access this at version 0.0.0 on GitHub.
Abhijit over at Stat Bandit posted some nice code for making forest plots using ggplot2 in R. You see these lots of times in meta-analyses, or as seen in the BioVU demonstration paper. The idea is simple - on the x-axis you have the odds ratio (or whatever stat you want to show), and each line is a different study, gene, SNP, phenotype, etc.
While preparing for my upcoming defense, I found a cool little web app called pubmed2wordle that turns a pubmed query into a word cloud using text from the titles and abstracts returned by the query. Here are the results for a pubmed query for me ("turner sd AND vanderbilt"): And quite different results for where I'm planning to do my postdoc: Looks useful to quickly get a sense of what other people work on.