Bayesian Joint Additive Factor Models for Multiview Learning

TL;DR

This paper introduces Bayesian joint additive factor models for multiview learning, enabling better interpretation, feature selection, and uncertainty quantification in multi-omics data analysis for precision medicine.

Contribution

It proposes two novel models, JFR and JAFAR, with new priors and inference algorithms, improving multiview data integration and prediction accuracy.

Findings

Enhanced prediction of clinical outcomes using multiview data.

Improved interpretability and feature selection in multiview models.

Demonstrated performance gains over state-of-the-art methods.

Abstract

It is increasingly common to collect data of multiple different types on the same set of samples. Our focus is on studying relationships between such multiview features and responses. A motivating application arises in the context of precision medicine where multi-omics data are collected to correlate with clinical outcomes. It is of interest to infer dependence within and across views while combining multimodal information to improve the prediction of outcomes. The signal-to-noise ratio can vary substantially across views, motivating more nuanced statistical tools beyond standard late and early fusion. This challenge comes with the need to preserve interpretability, select features, and obtain accurate uncertainty quantification. To address these challenges, we introduce two complementary factor regression models. A baseline Joint Factor Regression (\textsc{jfr}) captures combined variation across views via a single factor set, and a more nuanced Joint Additive FActor Regression (\textsc{jafar}) that decomposes variation into shared and view-specific components. For \textsc{jfr}, we use independent cumulative shrinkage process (\textsc{i-cusp}) priors, while for \textsc{jafar} we develop a dependent version (\textsc{d-cusp}) designed to ensure identifiability of the components. We develop Gibbs samplers that exploit the model structure and accommodate flexible feature and outcome distributions. Prediction of time-to-labor onset from immunome, metabolome, and proteome data illustrates performance gains against state-of-the-art competitors. Our open-source software (\texttt{R} package) is available at https://github.com/niccoloanceschi/jafar.