Statistical analysis of microbiome longitudinal data with multiplex networks

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Networks generally refer to graph-based representations of a set of discrete entities and their interactions. They are useful conceptual tools for analyzing biological systems and the complex relationships among their constituents; they can be easily applied to microbiome data in which individual entities (nodes) can be represented by individuals, time points, or geographic locations. Links (edges) connecting these nodes together can represent genetic similarities, or geographic distances, among others. There are several approaches to study microbiome longitudinal data, either to make forecasts or to detect shift in microbial communities. At the same time, there exist numerous network approaches currently available to take into account the increasing complexity of time series. For this proposal, I want to bridge the gap between such fields by (Axis 1) developing methods to efficiently analyze microbiome longitudinal data using multiplex networks, and then (Axis 2) applying such network techniques in a diversity of empirical projects for which microbiome samples have been collected at different time scales. Namely, the proposal is broken down in five distinct activities, In Activity 1.1, I will assess the performance of multiplex networks with simulated microbiome data, prior to using such methods to actual longitudinal data. In Activity 1.2, I will evaluate randomization models to analyze multiplex networks as temporal graphs and will develop a statistical test to do so. In Activity 2.1, I will analyze skin microbiome samples collected for the `1000 Handshakes project' to understand the dymamics of contamination. In Activity 2.2, I will analyze vaginal microbiome samples collected on pregnant women to assess whether specific changes in the communities can predict labor. In Activity 2.3, I will analyze microbiome samples collected on decomposing corpses donated to a body farm to estimate post-mortem interval. How to extract useful information from multilayer microbiome networks and assess their structure through time is a question of acute interest for researchers. For example, the relationships among microbes in a network, rather than variation in specific individual microbes, may underlie important functional differences. Yet, we lack the statistical framework to identify and fully compare networks among different microbial habitats over time. This proposal aims at filling this need by proposing novel approaches to analyze the dynamics and stability of microbial communities at multiple time scales using multiplex temporal networks.