Paper Info
Title
A Bayesian method for detecting pairwise associations in compositional data.
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.
Year
DOI
Venue
2017
10.1371/journal.pcbi.1005852
PLOS COMPUTATIONAL BIOLOGY
Field
DocType
Volume
Normalization (statistics),Uncertainty quantification,Biology,Inference,Lasso (statistics),Bioinformatics,Type I and type II errors,Covariance matrix,Covariance,Bayesian probability
Journal
13
Issue
ISSN
Citations 
11
1553-7358
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Schwager Emma101.01
Himel Mallick232.21
Ventz Steffen300.34
Curtis Huttenhower443830.18