High throughput biological data sets are growing exponentially both in size and complexity. Extracting meaningful information from this data requires not only programming skills, but also understanding of the analysis workflows and visualization tools that help to condense a large amount of information into a comprehensible story. This course will introduce ways to address the challenges of complex biological data sets using R tools and Bioconductor packages. The ideal participant is someone with programming experience (not necessarily in R), but has no hands-on experiences on managing and analyzing very large data sets. The course will cover R language basics and features relevant to the challenges presented by large biological data. This includes efficiently loading data into R, basic exploratory data analysis with workhorse R functions, and graphical visualization. The data analysis workflow from raw data to functional inferences and biological networks will be conducted using real world genomics and proteomics data.
2012/05/22 - update
2012/05/03 – update
The course location is confirmed as Meeting Room 2, at the UC Davis Activities & Recreation Center. The ARC is conveniently located along bus routes and is within walking distance of the Genome Center.