Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

ABSTRACT: The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC5154917 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature