MODELING ASSOCIATION IN MICROBIAL COMMUNITIES WITH CLIQUE LOGLINEAR MODELS

• 成果类型: Article
• 署名作者: Dobra, Adrian; Valdes, Camilo; Ajdic, Dragana; Clarke, Bertrand; Clarke, Jennifer
• 署名单位: University of Washington; University of Washington Seattle; State University System of Florida; Florida International University; University of Miami; University of Miami; University of Nebraska System; University of Nebraska Lincoln
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/18-AOAS1229
• 发表日期: 2019
• 页码: 931-+
• 关键词: shotgun stochastic search; Graphical Models; categorical-data; read alignment; Bayes Factors; selection; dimension; mixtures

摘要

There is a growing awareness of the important roles that microbial communities play in complex biological processes. Modern investigation of these often uses next generation sequencing of metagenomic samples to determine community composition. We propose a statistical technique based on clique loglinear models and Bayes model averaging to identify microbial components in a metagenomic sample at various taxonomic levels that have significant associations. We describe the model class, a stochastic search technique for model selection, and the calculation of estimates of posterior probabilities of interest. We demonstrate our approach using data from the Human Microbiome Project and from a study of the skin microbiome in chronic wound healing. Our technique also identifies significant dependencies among microbial components as evidence of possible microbial syntrophy.