This is a guest post from VP Nagraj, a data scientist embedded within UVA’s Health Sciences Library, who runs our Data Analysis Support Hub (DASH) service. The What GRUPO (Gauging Research University Publication Output) is a Shiny app that provides side-by-side benchmarking of American research university publication activity.

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.

I work with gene lists on a nearly daily basis. Lists of genes near ChIP-seq peaks, lists of genes closest to a GWAS hit, lists of differentially expressed genes or transcripts from an RNA-seq experiment, lists of genes involved in certain pathways, etc. And lots of times I’ll need to convert these gene IDs from one identifier to another. There’s no shortage of tools to do this. I use Ensembl Biomart.

I just returned from the Genome Informatics meeting at Cold Spring Harbor. This was, hands down, the best scientific conference I've been to in years. The quality of the talks and posters was excellent, and it was great meeting in person many of the scientists and developers whose tools and software I use on a daily basis.

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.

Per tradition, Russ Altman gave his "Translational Bioinformatics: The Year in Review" presentation at the close of the AMIA Joint Summit on Translational Bioinformatics in San Francisco on March 26th.  This year, papers came from six key areas (and a final Odds and Ends category).  His full slide deck is available here.

At our most recent R user group meeting we were delighted to have presentations from Mark Lawson and Steve Hoang, both bioinformaticians at Hemoshear. All of the code used in both demos is in our Meetup’s GitHub repo.

Anscombe’s quartet comprises four datasets that have nearly identical simple statistical properties, yet appear very different when graphed. Each dataset consists of eleven ( x , y ) points. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance of graphing data before analyzing it and the effect of outliers on statistical properties. Let’s load and view the data.

Current Opinion in Microbiology recently published a special issue in genomics. In an excellent editorial overview, “Genomics: The era of genomically-enabled microbiology”, Neil Hall and Jay Hinton give an overview of the state of the field in microbial genomics, summarize recent contributions, and give a great synopsis of each of the reviews in this issue.

Joanna Zhao’s and Jenny Bryan’s R graph catalog is meant to be a complement to the physical book, Creating More Effective Graphs, but it’s a really nice gallery in its own right. The catalog shows a series of different data visualizations, all made with R and ggplot2. Click on any of the plots and you get the R code necessary to generate the data and produce the plot.