August 8–10. Iowa State University, Ames, Iowa

Sponsored by:


ASA Sections on Statistical Graphics and Computing


Analyzing transcriptomic, metabolomic and biological network data using R, Bioconductor and GGobi

Michael Lawrence, Iowa State University

This tutorial introduces the practical use of R, Bioconductor, and GGobi for the analysis of transcriptomic, metabolomic, and network data. The course is divided into three sections. The first and second sections discuss the import and analysis of microarray and chromatography mass spectrometry data, respectively. The final section introduces exploRase, a graphical user interface (GUI) for the integrated analysis and interactive visualization of experimental data and networks.

The first session begins with the import and preprocessing of microarray data using tools in Bioconductor. Special attention is paid to the use of diagnostic plots to look for problems in the data and to double check preprocessing results. The session concludes with a survey of the methods available in R for finding differentially expressed and co-expressed genes.

The focus shifts to metabolomics for the second session, which concentrates on the preprocessing of chromatography mass spectrometry data. We introduce methods available in R packages for denoising the data and detecting chromatographic peaks, followed by the identification and quantification of metabolites. Interactive GGobi plots are used throughout the analysis to inspect the output of the algorithms.

The tutorial concludes with an explanation of the exploRase package. ExploRase uses R and Bioconductor for data analysis and relies on GGobi for interactive plots of experimental data and networks. We will give an overview of the functionality in exploRase and carry out an example data analysis.

The tutorial is primarily designed for an audience of biologists, but other researchers working with bioinformatics data might also find it useful. Attendees will be given the opportunity to practice the techniques on their own data.