Title | A Bayesian method for detecting pairwise associations in compositional data |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Schwager E, Mallick H, Ventz S, Huttenhower C |
Journal | PLoS Comput Biol |
Volume | 13 |
Issue | 11 |
Pagination | e1005852 |
Date Published | 2017 Nov |
ISSN | 1553-7358 |
Keywords | Algorithms, Bayes Theorem, Computational Biology, Computer Simulation, Ecology, Humans, Markov Chains, Microbiota, Models, Biological, Proteobacteria |
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. |
DOI | 10.1371/journal.pcbi.1005852 |
Alternate Journal | PLoS Comput Biol |
PubMed ID | 29140991 |
PubMed Central ID | PMC5706738 |
Grant List | R01 HG005220 / HG / NHGRI NIH HHS / United States T32 GM074897 / GM / NIGMS NIH HHS / United States U54 DE023798 / DE / NIDCR NIH HHS / United States U54 DK102557 / DK / NIDDK NIH HHS / United States |