B1664
Title: Multiple augmented reduced rank regression for pan-cancer analysis
Authors: Jiuzhou Wang - University of Minnesota (United States) [presenting]
Eric Lock - University of Minnesota (United States)
Abstract: Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (i.e., cohorts), multiple augmented reduced rank regression (maRRR), a flexible matrix regression and a factorization method are proposed to concurrently learn both covariate-driven and auxiliary structured variation. A structured nuclear norm objective is considered that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. The framework subsumes several existing methods, such as reduced rank regression and unsupervised multi-matrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variation. MaRRR is applied to gene expression data from multiple cancer types (i.e., pan-cancer) from TCGA, with somatic mutations as covariates. The method performs well with respect to the prediction and imputation of held-out data and provides new insights into the mutation-driven and auxiliary variation that is shared or specific to certain cancer types.
