# Highlights of the Milwaukee Workshop on R and Bioinformatics

**Revolutions**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

*by Joseph Rickert*

On May 10th and 11th, in honor of this being the International Year of Statistics, the Milwaukee Chapter of the American Statistical Association (MILWASA) held a workshop on cutting edge uses of R in Bioinformatics. One objective of the workshop was to show the “nuts and bolts” details of how R with C++ integration and the specialized capabilities of the Bioconductor Project provides an flexible, feature-rich platform for advanced Bioinfomatics applications. Featured speakers were:

- Denise Scholtens who gave talks on analyzing microarray data using R and Bioconductor, building graphs with R and Bioconductor, gene set enrichment analysis, and Expression Set objects.
- Kwang-Youn Kim who spoke on the analysis of RNA sequencing data using R and Bioconductor and
- Dirk Eddelbuettel gave a thorough, four-part introduction to Rcpp, his package for integrating R with C++.

A tremendous amount of material from this workshop (pdfs, slides, data and R code) is available online. And, if you are interested in R and C++ integration have a look at Dirk’s new book.

The following graph from Denise’s presentation on gene set enrichment analysis shows a portion of an induced gene ontology graph using using the classic Fisher elimination algorithm and gives an idea of the some of the sophisticated analyses you can do with her R code.

Note if you want to run the code you will have to get some of the packages from Bioconductor. Here is some code from Kwang-Youn on how to get started.

# Install all the necessary packages if not on your system source("http://bioconductor.org/biocLite.R") ## Bioconductor version 2.11 (BiocInstaller 1.8.3), ?biocLite for help biocLite(c("TxDb.Dmelanogaster.UCSC.dm3.ensGene", "ShortRead", "edgeR", "cummeRbund")) ## BioC mirror: http://bioconductor.org ## Using Bioconductor version 2.11 (BiocInstaller 1.8.3), R version 2.15. ## Installing package(s) 'TxDb.Dmelanogaster.UCSC.dm3.ensGene' 'ShortRead' ## 'edgeR' 'cummeRbund' ## ## The downloaded binary packages are in ## /var/folders/nk/9bnzzk_152vg4wslcbxc5g_c0000gn/T//RtmpzkrvEW/downloaded_packages

And here is some sample code from Dirk's presentation on calling R plot functions from C++.

#include// embedded R via RInside int main(int argc, char *argv[]) { RInside R(argc, argv); // create an embedded R instance // evaluate an R expression with curve() std::string cmd = "tmpf <- tempfile(’curve’); " "png(tmpf); curve(x^2, -10, 10, 200); " "dev.off(); tmpf"; // by running parseEval, we get ﬁlename back std::string tmpfile = R.parseEval(cmd); std::cout << "Could use plot in " << tmpfile << std::endl; unlink(tmpfile.c_str()); // cleaning up // alternatively, by forcing a display we can plot to screen cmd = "x11(); curve(x^2, -10, 10, 200); Sys.sleep(30);"; R.parseEvalQ(cmd); exit(0); }

Revolution Analytics is proud to have been a sponsor for this workshop. Congratulations to Rodney Sparapani of the Medical College of Wisconsin for making it happen!

**leave a comment**for the author, please follow the link and comment on their blog:

**Revolutions**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.