Two-sample goodness-of-fit tests on the flat torus based on Wasserstein distance and their relevance to structural biology

TL;DR

This paper develops Wasserstein distance-based two-sample goodness-of-fit tests on the flat torus, extending optimal transport theory and applying them to analyze local protein structures in biology.

Contribution

It introduces new Wasserstein-based testing methods on the flat torus, extending optimal transport theory with a Central Limit Theorem, and demonstrates their application to protein structure data.

Findings

Validated the proposed tests through numerical experiments.

Proved the tests' validity and consistency.

Applied tests to real protein data.

Abstract

This work is motivated by the study of local protein structure, which is defined by two variable dihedral angles that take values from probability distributions on the flat torus. Our goal is to provide the space $\mathcal{P}(\mathbb{R}^2/\mathbb{Z}^2)$ with a metric that quantifies local structural modifications due to changes in the protein sequence, and to define associated two-sample goodness-of-fit testing approaches. Due to its adaptability to the space geometry, we focus on the Wasserstein distance as a metric between distributions. We extend existing results of the theory of Optimal Transport to the $d$-dimensional flat torus $\mathbb{T}^d=\mathbb{R}^d/\mathbb{Z}^d$, in particular a Central Limit Theorem. Moreover, we propose different approaches for two-sample goodness-of-fit testing for the one and two-dimensional case, based on the Wasserstein distance. We prove their validity and consistency. We provide an implementation of these tests in \textsf{R}. Their performance is assessed by numerical experiments on synthetic data and illustrated by an application to protein structure data.