Methods and Theory for Estimating Individual-Specific and Cell-Type-Specific Gene Networks

In the existing literature on biological network analyses, most approaches assume a common or stratified network structure across subjects. Consequently, they are not able to flexibly account for heterogeneity in individual-level networks. For example, the individual-level networks may differ due to the complex effects of genetic variants, sex, and varying compositions across biological samples. Characterizing such network heterogeneity presents an urgent need for new statistical methodology and theory. Motivated by gene co-expression analyses, this project aims to make substantial progress in network analysis with heterogeneity. The developed methods can impact a wide range of topics in human genetics and genomics, precision health, and medicine; they are also more broadly applicable to scientific fields such as neuroscience, finance, and social science. The PI will integrate research into education by training undergraduate and graduate students and developing special topics courses. This project aims to provide novel and fundamental perspectives on the emerging challenges in estimating high-dimensional covariances with heterogeneity. The first part of the project breaks new ground on estimating individual-specific graphical models. The PI will develop a new graphical regression model that relates the conditional dependence structure to covariates of high dimensions. The second part addresses the challenge in inferring cell-type-specific gene networks from aggregated data with different compositions. The PI will develop a flexible framework that does not make specific assumptions on the distributions of expressions and consider a novel least squares estimation. The developed methods in this project have appealing features, including identifiability and interpretability, efficient computation, quantifiable statistical errors, and valid statistical inference. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.