BIDIMENSIONAL LINKED MATRIX FACTORIZATION FOR PAN-OMICS PAN-CANCER ANALYSIS

• Published in: The annals of applied statistics Vol. 16; no. 1; p. 193
• Main Authors: Lock, Eric F, Park, Jun Young, Hoadley, Katherine A
• Format: Journal Article
• Language: English
• Published: United States 01.03.2022
• Subjects: nuclear norm penalization; low-rank matrix factorization; data integration; Cancer genomics; missing data imputation
• ISSN: 1932-6157
• DOI: 10.1214/21-AOAS1495

Several modern applications require the integration of multiple large data matrices that have shared rows and/or columns. For example, cancer studies that integrate multiple omics platforms across multiple types of cancer, , have extended our knowledge of molecular heterogeneity beyond what was observed in single tumor and single platform studies. However, these studies have been limited by available statistical methodology. We propose a flexible approach to the simultaneous factorization and decomposition of variation across such matrices, BIDIFAC+. BIDIFAC+ 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., cancer types). This builds on a growing literature for the factorization and decomposition of linked matrices which has primarily focused on multiple matrices that are linked in one dimension (rows or columns) only. Our objective function extends nuclear norm penalization, is motivated by random matrix theory, gives a unique decomposition under relatively mild conditions, and can be shown to give the mode of a Bayesian posterior distribution. We apply BIDIFAC+ to pan-omics pan-cancer data from TCGA, identifying shared and specific modes of variability across different omics platforms and 29 different cancer types.