Probing the Geometry of Data with Diffusion Fr\'echet Functions

TL;DR

This paper introduces diffusion Fréchet functions and vectors as stable, shape-describing tools for analyzing probability distributions on networks and Euclidean spaces, with applications to gut microbiome data.

Contribution

It develops a novel framework for modeling data organization across scales using diffusion-based metrics, providing robust shape descriptors with proven stability.

Findings

Applied to gut microbiome data, revealing divergence in health states.

Identified potential biomarkers for Clostridium difficile infection.

Demonstrated robustness of the method against distribution perturbations.

Abstract

Many complex ecosystems, such as those formed by multiple microbial taxa, involve intricate interactions amongst various sub-communities. The most basic relationships are frequently modeled as co-occurrence networks in which the nodes represent the various players in the community and the weighted edges encode levels of interaction. In this setting, the composition of a community may be viewed as a probability distribution on the nodes of the network. This paper develops methods for modeling the organization of such data, as well as their Euclidean counterparts, across spatial scales. Using the notion of diffusion distance, we introduce diffusion Frechet functions and diffusion Frechet vectors associated with probability distributions on Euclidean space and the vertex set of a weighted network, respectively. We prove that these functional statistics are stable with respect to the Wasserstein distance between probability measures, thus yielding robust descriptors of their shapes. We apply the methodology to investigate bacterial communities in the human gut, seeking to characterize divergence from intestinal homeostasis in patients with Clostridium difficile infection (CDI) and the effects of fecal microbiota transplantation, a treatment used in CDI patients that has proven to be significantly more effective than traditional treatment with antibiotics. The proposed method proves useful in deriving a biomarker that might help elucidate the mechanisms that drive these processes.