Statistical analysis of microbiome longitudinal data with multiplex networks

利用多重网络对微生物组纵向数据进行统计分析

• 批准号: RGPIN-2021-03120
• 负责人: Lapointe, FrançoisJoseph
• 金额: $ 2.04万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765217
• 关键词: Statistical; analysis; microbiome; longitudinal; data

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.

网络通常是指基于图的一组离散实体及其交互的表示。它们是分析生物系统及其成分之间复杂关系的有用概念工具；它们可以轻松地应用于微生物组数据中，其中个体，时间点或地理位置可以代表单个实体（节点）。将这些节点连接在一起的链接（边缘）可以代表遗传相似性或地理距离等。有几种研究微生物组纵向数据的方法，以进行预测或检测微生物群落的转变。同时，目前有许多网络方法可以考虑到时间序列的复杂性增加。对于此提案，我想通过（AXIS 1）开发使用多重网络有效分析微生物组纵向数据的方法之间的差距，然后（AXIS 2）将这种网络技术应用于多样性的经验项目中，以在不同的时间量表中收集了微生物组样品。即，在活动1.1中，该提案在五个不同的活动中被分解，我将在使用这种方法对实际纵向数据之前评估具有模拟微生物组数据的多重网络的性能。在活动1.2中，我将评估随机模型以将多路复用网络分析为时间图，并将开发统计检验。在活动2.1中，我将分析为“ 1000个握手项目”收集的皮肤微生物组样品，以了解污染的二合性。在活动2.2中，我将分析对孕妇收集的阴道微生物组样本，以评估社区中的特定变化是否可以预测劳动。在活动2.3中，我将分析在捐赠给身体农场的分解尸体上收集的微生物组样品，以估计验尸间隔。如何从多层微生物组网络中提取有用的信息并通过时间评估其结构是研究人员急剧兴趣的问题。例如，网络中微生物之间的关系，而不是特定单个微生物的变化，可能是重要的功能差异的基础。但是，我们缺乏统计框架，无法随时间识别和完全比较不同微生物栖息地之间的网络。该建议旨在通过提出新的方法来填补这种需求，以使用多重时间网络在多个时间尺度上分析微生物群落的动态和稳定性。