Bayesian integrative factor analysis methods, with application in nutrition and genomics data

TL;DR

This paper reviews and compares five advanced Bayesian integrative factor analysis methods, demonstrating their application to high-dimensional nutrition and genomics data to improve data integration, interpretability, and reproducibility.

Contribution

It provides a practical guide, including R code, to apply and compare five Bayesian integrative factor models for multi-study data analysis in biomedical research.

Findings

Bayesian methods outperform standard FA in accuracy and robustness.

The tutorial includes real-data applications in nutrition and genomics.

Computational efficiency varies across methods, influencing practical choices.

Abstract

High-dimensional data are crucial in biomedical research. Integrating such data from multiple studies is a critical process that relies on the choice of advanced statistical models, enhancing statistical power, reproducibility, and scientific insight compared to analyzing each study separately. Factor analysis (FA) is a core dimensionality reduction technique that models observed data through a small set of latent factors. Bayesian extensions of FA have recently emerged as powerful tools for multi-study integration, enabling researchers to disentangle shared biological signals from study-specific variability. In this tutorial, we provide a practical and comparative guide to five advanced Bayesian integrative factor models: Perturbed Factor Analysis (PFA), Bayesian Factor Regression with non-local spike-and-slab priors (MOM-SS), Subspace Factor Analysis (SUFA), Bayesian Multi-study Factor Analysis (BMSFA), and Bayesian Combinatorial Multi-study Factor Analysis (Tetris). To contextualize these methods, we also include two benchmark approaches: standard FA applied to pooled data (Stack FA) and FA applied separately to each study (Ind FA). We evaluate all methods through extensive simulations, assessing computational efficiency and accuracy in the estimation of loadings and number of factors. To bridge theory and practice, we present a full analytical workflow, with detailed R code, demonstrating how to apply these models to real-world datasets in nutrition and genomics. This tutorial is designed to guide applied researchers through the landscape of Bayesian integrative factor analysis, offering insights and tools for extracting interpretable, robust patterns from complex multi-source data. All code and resources are available at: https://github.com/Mavis-Liang/Bayesian_integrative_FA_tutorial