Dynamic factor analysis for sparse and irregular longitudinal data: an application to metabolite measurements in a COVID-19 study

TL;DR

This paper introduces a dynamic factor analysis model for sparse, irregular longitudinal biomarker data that accounts for pathway interactions, demonstrated through COVID-19 metabolite measurements, leading to novel biomarker discoveries.

Contribution

It proposes a new dynamic factor analysis approach with cross-correlation modeling via multi-output Gaussian processes, tailored for large-scale, sparse longitudinal data.

Findings

Identified a kynurenine pathway influencing COVID-19 severity.

Discovered taurine as a novel biomarker relevant to disease progression.

StEM algorithm improved hyperparameter estimation accuracy.

Abstract

It is of scientific interest to identify essential biomarkers in biological processes underlying diseases to facilitate precision medicine. Factor analysis (FA) has long been used to address this goal: by assuming latent biological pathways drive the activity of measurable biomarkers, a biomarker is more influential if its absolute factor loading is larger. Although correlation between biomarkers has been properly handled under this framework, correlation between latent pathways are often overlooked, as one classical assumption in FA is the independence between factors. However, this assumption may not be realistic in the context of pathways, as existing biological knowledge suggests that pathways interact with one another rather than functioning independently. Motivated by sparsely and irregularly collected longitudinal measurements of metabolites in a COVID-19 study of large sample size, we propose a dynamic factor analysis model that can account for the potential cross-correlations between pathways, through a multi-output Gaussian processes (MOGP) prior on the factor trajectories. To mitigate against overfitting caused by sparsity of longitudinal measurements, we introduce a roughness penalty upon MOGP hyperparameters and allow for non-zero mean functions. To estimate these hyperparameters, we develop a stochastic expectation maximization (StEM) algorithm that scales well to the large sample size. In our simulation studies, StEM leads across all sample sizes considered to a more accurate and stable estimate of the MOGP hyperparameters than a comparator algorithm used in previous research. Application to the motivating example identifies a kynurenine pathway that affects the clinical severity of patients with COVID-19. In particular, a novel biomarker taurine is discovered, which has been receiving increased attention clinically, yet its role was overlooked in a previous analysis.