Journal
ANNALS OF APPLIED STATISTICS
Volume 13, Issue 2, Pages 931-+Publisher
INST MATHEMATICAL STATISTICS-IMS
DOI: 10.1214/18-AOAS1229
Keywords
contingency tables; graphical models; model selection; micro-biome; next generation sequencing
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Funding
- National Science Foundation [DMS/MPS-1737746, DMS-1120255, DMS-1410771, DMS-1419754]
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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.
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