STOR Colloquium: Eric Lock, University of Minnesota

Eric Lock

University of Minnesota School of Public Health

Bidimensional Linked Matrix Decomposition for

Pan-Omics Pan-Cancer Analysis

Several recent methods address the integrative dimension reduction and decomposition of linked high‐content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set with features from different platforms (vertical integration). This is limiting for data that take the form of bidimensionally linked matrices, e.g., multiple molecular omics platforms measured for multiple sample cohorts, which are increasingly common in biomedical studies. We propose a flexible approach to the simultaneous factorization and decomposition of variation across bidimensionally linked matrices, BIDIFAC+. This decomposes variation into a series of low-rank components that may be shared across any number of row sets (e.g., omics platforms) or column sets (e.g., sample cohorts). Our objective function extends nuclear norm penalization, is motivated by random matrix theory, and can be shown to give the mode of a Bayesian posterior distribution. We apply the method to pan-omics pan-cancer data from The Cancer Genome Atlas (TCGA), integrating data from 4 different omics platforms and 29 different cancer types

Refreshments will be served at 3:00pm in the 3^rd floor lounge of Hanes Hall