Heterogeneous large datasets integration using Bayesian factor regression

TL;DR

This paper introduces a scalable Bayesian factor regression model that effectively integrates heterogeneous large datasets, corrects for batch effects, and improves dimensionality reduction accuracy, especially in bioinformatics applications.

Contribution

It proposes a novel sparse latent factor regression model with a scalable EM algorithm and non-local priors, enhancing data integration and batch effect correction.

Findings

Non-local priors improve factor cardinality reconstruction.

Model increases accuracy of dimensionality reduction.

Effective correction for complex batch effects.

Abstract

Two key challenges in modern statistical applications are the large amount of information recorded per individual, and that such data are often not collected all at once but in batches. These batch effects can be complex, causing distortions in both mean and variance. We propose a novel sparse latent factor regression model to integrate such heterogeneous data. The model provides a tool for data exploration via dimensionality reduction while correcting for a range of batch effects. We study the use of several sparse priors (local and non-local) to learn the dimension of the latent factors. Our model is fitted in a deterministic fashion by means of an EM algorithm for which we derive closed-form updates, contributing a novel scalable algorithm for non-local priors of interest beyond the immediate scope of this paper. We present several examples, with a focus on bioinformatics applications. Our results show an increase in the accuracy of the dimensionality reduction, with non-local priors substantially improving the reconstruction of factor cardinality, as well as the need to account for batch effects to obtain reliable results. Our model provides a novel approach to latent factor regression that balances sparsity with sensitivity and is highly computationally efficient.