Statistical analysis of microbiome longitudinal data with multiplex networks

网络通常是指一组离散实体及其相互作用的基于图的表示。它们是分析生物系统及其组成部分之间复杂关系的有用概念工具；它们可以很容易地应用于微生物组数据，其中单个实体（节点）可以由个体、时间点或地理位置表示。将这些节点连接在一起的链接（边）可以表示遗传相似性或地理距离等。有几种方法来研究微生物组的纵向数据，要么做出预测，要么检测微生物群落的变化。同时，考虑到时间序列日益增加的复杂性，目前已有许多可用的网络方法。对于这个提议，我想通过（轴1）开发方法来有效地分析微生物组纵向数据使用多路网络，然后（轴2）应用这种网络技术在不同时间尺度上收集微生物组样本的各种经验项目之间的差距。也就是说，该提案被分解为五个不同的活动。在活动1.1中，我将使用模拟微生物组数据评估复用网络的性能，然后将这些方法用于实际的纵向数据。在活动1.2中，我将评估随机化模型来分析多重网络作为时间图，并将为此开发一个统计测试。在活动2.1中，我将分析为“1000次握手项目”收集的皮肤微生物组样本，以了解污染的动态。在活动2.2中，我将分析孕妇阴道微生物组样本，以评估群落的具体变化是否可以预测分娩。在活动2.3中，我将分析从捐赠给尸体农场的腐烂尸体上收集的微生物组样本，以估计死后时间。如何从多层微生物组网络中提取有用信息并评估其随时间变化的结构是研究人员非常感兴趣的问题。例如，一个网络中微生物之间的关系，而不是特定个体微生物的变异，可能是重要功能差异的基础。然而，我们缺乏统计框架来识别和全面比较不同微生物栖息地之间的网络。本提案旨在通过提出新的方法来分析微生物群落在多时间尺度上使用多重时间网络的动态和稳定性，以填补这一需求。

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.