Estimating Gaussian graphical models of multi-study data with Multi-Study Factor Analysis

TL;DR

This paper introduces MSFA-X, a novel statistical framework that extends multi-study factor analysis to estimate Gaussian graphical models, enabling the analysis of shared and study-specific network structures in complex biological multi-study data.

Contribution

The paper develops MSFA-X, a new method that generalizes MSFA to estimate GGMs, improving network modeling of multi-study biological data.

Findings

MSFA-X accurately recovers shared and study-specific GGMs in simulations.

MSFA-X outperforms graphical lasso in network recovery tasks.

Application to HAPO data reveals differences in glucose metabolism networks.

Abstract

Network models are powerful tools for gaining new insights from complex biological data. Most lines of investigation in biology involve comparing datasets in the setting where the same predictors are measured across multiple studies or conditions (multi-study data). Consequently, the development of statistical tools for network modeling of multi-study data is a highly active area of research. Multi-study factor analysis (MSFA) is a method for estimation of latent variables (factors) in multi-study data. In this work, we generalize MSFA by adding the capacity to estimate Gaussian graphical models (GGMs). Our new tool, MSFA-X, is a framework for latent variable-based graphical modeling of shared and study-specific signals in multi-study data. We demonstrate through simulation that MSFA-X can recover shared and study-specific GGMs and outperforms a graphical lasso benchmark. We apply MSFA-X to analyze maternal response to an oral glucose tolerance test in targeted metabolomic profiles from the Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) Study, identifying network-level differences in glucose metabolism between women with and without gestational diabetes mellitus.