Multi-study Factor Analysis

TL;DR

This paper presents a new factor analysis method for jointly analyzing multiple studies to identify shared and study-specific factors, improving understanding of cross-study reproducibility in multivariate data.

Contribution

It introduces a novel joint factor analysis approach with an efficient algorithm and a procedure for selecting common and specific factors, advancing multi-study data analysis.

Findings

Method performs well in simulations.

Application to ovarian cancer gene expression data demonstrates benefits.

Joint analysis clarifies cross-study reproducibility.

Abstract

We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate 1) common factors shared across multiple studies, and 2) study-specific factors. We develop a fast Expectation Conditional-Maximization algorithm for parameter estimates and we provide a procedure for choosing the common and specific factor. We present simulations evaluating the performance of the method and we illustrate it by applying it to gene expression data in ovarian cancer. In both cases, we clarify the benefits of a joint analysis compared to the standard factor analysis. We hope to have provided a valuable tool to accelerate the pace at which we can combine unsupervised analysis across multiple studies, and understand the cross-study reproducibility of signal in multivariate data.