Uncovering Pathway-Centric Information from Heterogeneous Data
Gene Set Variation Analysis (GSVA) is an advanced type of gene set enrichment (GSE) method designed to condense complex gene expression profiles into more interpretable pathway or signature summaries. Unlike traditional GSE methods that focus on simple case-control studies, GSVA assesses the variation of pathway activity over the sample population in an unsupervised manner. This approach is particularly effective for analyzing highly heterogeneous datasets
and can serve as a starting point for pathway-centric modeling. In this tutorial, we will introduce the intuition behind GSVA and its methodological framework. Participants will be guided through a practical application of GSVA using R to analyze a gene expression microarray dataset. The session will cover essential steps, including data preparation, calculation of GSVA enrichment scores, and potential downstream analyses like differential expression analysis. Through this tutorial, attendees will be equipped with the skills and understanding necessary to implement GSVA in their own research, enhancing their ability to decipher complex biological data.
GSVA paper: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7
About the speaker Dr. Pan Liu: http://jsb.ucla.edu/people/pan-liu