Modeling association in microbial communities with clique loglinear models

Adrian Dobra, Camilo Valdes, Dragana Ajdic, Bertrand Clarke, Jennifer Clarke

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)931-957
Number of pages27
JournalAnnals of Applied Statistics
Issue number2
StatePublished - 2019


  • Contingency tables
  • Graphical models
  • Microbiome
  • Model selection
  • Next generation sequencing

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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