Background This tutorial shows an example of RNA-seq data analysis with DESeq2, followed by KEGG pathway analysis using GAGE. Using data from GSE37704, with processed data available on Figshare DOI: 10.6084/m9.figshare.1601975.

The problem I was looking for a way to compile an RMarkdown document and have the filename of the resulting PDF or HTML document contain the name of the input data that it processed. That is, if I compiled the analysis.Rmd file, where in that file it did some analysis and reporting on data001.txt, I’d want the resulting filename to look something like data001.txt.analysis.html.

I forgot where I originally found the code to do this, but I recently had to dig it out again to remind myself how to draw two different y axes on the same plot to show the values of two different features of the data. This is somewhat distinct from the typical use case of aesthetic mappings in ggplot2 where I want to have different lines/points/colors/etc. for the same feature across multiple subsets of data.

Last week I ran a one-day workshop on RNA-seq data analysis in the UVA Health Sciences Library. I set up an AWS public EC2 image with all the necessary software installed.

Data “janitor-work” The New York Times recently ran a piece on wrangling and cleaning data: “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights” Whether you call it “janitor-work,” wrangling/munging, cleaning/cleansing/scrubbing, tidying, or something else, the article above is worth a read (even though it implicitly denigrates the important work that your housekeeping staff does). It’s one of the few “Big Data” pieces that

Last week I taught a three-hour introduction to R workshop for life scientists at UVA's Health Sciences Library. I broke the workshop into three sections: In the first half hour or so I presented slides giving an overview of R and why R is so awesome. During this session I emphasized reproducible research and gave a demonstration of using knitr + rmarkdown in RStudio to produce a PDF that can easily be recompiled when data updates.

A couple of months ago I posted about how to visualize exome coverage with bedtools and R. But if you're looking to get a basic handle on genome arithmetic, take a look at Aaron Quinlan's bedtools tutorials from the 2013 CSHL course.

Three years ago I wrote a blog post on how to create manhattan plots in R. After hundreds of comments pointing out bugs and other issues, I've finally cleaned up this code and turned it into an R package.

Software Carpentry is an international collaboration backed by Mozilla and the Sloan Foundation comprising a team of volunteers that teach computational competence and basic programming skills to scientists.

I first mentioned Coursera about a year ago, when I hired a new analyst in my core. This new hire came in as a very competent Python programmer with a molecular biology and microbial ecology background, but with very little experience in statistics.