HyperTraPS: Inferring probabilistic patterns of trait acquisition in evolutionary and disease progression pathways

TL;DR

HyperTraPS is a versatile statistical platform that infers probabilistic pathways of trait acquisition or loss in biomedical systems, effectively analyzing diverse datasets to uncover dynamic progression mechanisms.

Contribution

The paper introduces HyperTraPS, a novel Bayesian framework capable of efficiently inferring complex trait progression pathways from various data types in biomedical research.

Findings

Successfully applied to ovarian cancer progression

Revealed new pathways in multidrug-resistant tuberculosis

Quantified uncertainty in pathway predictions

Abstract

The explosion of data throughout the biomedical sciences provides unprecedented opportunities to learn about the dynamics of evolution and disease progression, but harnessing these large and diverse datasets remains challenging. Here, we describe a highly generalisable statistical platform to infer the dynamic pathways by which many, potentially interacting, discrete traits are acquired or lost over time in biomedical systems. The platform uses HyperTraPS (hypercubic transition path sampling) to learn progression pathways from cross-sectional, longitudinal, or phylogenetically-linked data with unprecedented efficiency, readily distinguishing multiple competing pathways, and identifying the most parsimonious mechanisms underlying given observations. Its Bayesian structure quantifies uncertainty in pathway structure and allows interpretable predictions of behaviours, such as which symptom a patient will acquire next. We exploit the model's topology to provide visualisation tools for intuitive assessment of multiple, variable pathways. We apply the method to ovarian cancer progression and the evolution of multidrug resistance in tuberculosis, demonstrating its power to reveal previously undetected dynamic pathways.