Berlin, 11-15 March 2019
High throughput (HT) techniques such as transcriptomics or metabolomics are of great significance in many areas of biology. However, the path from a boring list of differentially expressed genes to a biological understanding of the results is not straightforward.
This course offers computational techniques that go beyond a simple technical or statistical analysis. It covers techniques for the analysis of gene set enrichments, pathway analysis, gene ontologies, functional analysis of metabolomic profiling and making use of correlations and coexpression networks. A prominent part of the course will be devoted to data visualization and visual data exploration.
The students will gain the ability to independently process and analyse HT data sets, select the appropriate tools, functionally interpret the results as well as learn the paradigms of computational biology and statistics which will allow them to efficiently communicate with computational biologists.
As an incentive, each student will receive a set of gene expression profiles for a different organism, and during the course they will use these to generate species-specific gene expression modules and test their utility. If we are successfull, we will attempt a joint publication.
• Gene set enrichments in R
• Using GSEA for work in your own organism
• Applications of GSEA in:
– transcriptomics / RNASeq
– metabolomics and other platforms
– multivariate analyses
– combining data sets
– single cell RNASeq