Bio: Data Integration - TCGA

This working group will focus on the many statistical and computational challenges arising from the analysis and interpretation of the diverse types of genomics and proteomics data collected from the Cancer Genome Atlas (TCGA) Project (http://cancergenome.nih.gov). The main driving biological problems are the inference of cancer subtypes, and multidimensional outcome predictions through the joint analysis of diverse data types. Statistical tools to be explored include graphical models, pathway/network analysis. The major theme will be horizontal and vertical data integration. The working group will spend the first several weeks to get group members familiar with the TCGA project, the nature and information offered by each data type, state of the art statistical and computational methods that have been applied to the TCGA data, the knowledge learned from the TCGA project, and the many challenges remaining for cancer biology. We will also use the first month to collect and set up data access. The group will then identify biological problems to focus on and explore various statistical methods that can be applied/developed to address these problems. We expect each member will be actively participating through paper reviews, methodology developments and/or applications, collaborations, and reports.