A Bayesian method for detecting pairwise associations in compositional data. Academic Article uri icon

Overview

abstract

  • Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.

publication date

  • November 15, 2017

Research

keywords

  • Bayes Theorem
  • Computational Biology
  • Computer Simulation
  • Models, Biological

Identity

PubMed Central ID

  • PMC5706738

Scopus Document Identifier

  • 85036464214

Digital Object Identifier (DOI)

  • 10.1126/science.1224820

PubMed ID

  • 29140991

Additional Document Info

volume

  • 13

issue

  • 11