Probabilistic pathway-based multimodal factor analysis

TL;DR

This paper introduces PathFA, a new method for analyzing multiple types of biomedical data together, focusing on pathways to generate interpretable results for small sample sizes.

Contribution

PathFA is a novel Bayesian multimodal factor analysis method that integrates pathway-based information for interpretable results.

Findings

PathFA performs well on small sample sizes in simulations and real tumor data.

It recovers pathway activity linked to poor patient outcomes in melanoma.

The method identifies pathways associated with cell-type presence and tumor heterogeneity.

Abstract

Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations.

We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts.

The tool is implemented in python and available at https://github.com/ratschlab/path-fa

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