Paper Info
Title
A general framework for association analysis of microbial communities on a taxonomic tree.
Abstract
Motivation: Association analysis of microbiome composition with disease-related outcomes provides invaluable knowledge towards understanding the roles of microbes in the underlying disease mechanisms. Proper analysis of sparse compositional microbiome data is challenging. Existing methods rely on strong assumptions on the data structure and fail to pinpoint the associated microbial communities. Results: We develop a general framework to: (i) perform robust association tests for the microbial community that exhibits arbitrary inter-taxa dependencies; (ii) localize lineages on the taxonomic tree that are associated with covariates (e.g. disease status); and (iii) assess the overall association of the whole microbial community with the covariates. Unlike existing methods for microbiome association analysis, our framework does not make any distributional assumptions on the microbiome data; it allows for the adjustment of confounding variables and accommodates excessive zero observations; and it incorporates taxonomic information. We perform extensive simulation studies under a wide-range of scenarios to evaluate the new methods and demonstrate substantial power gain over existing methods. The advantages of the proposed framework are further demonstrated with real datasets from two microbiome studies. The relevant R package miLineage is publicly available.
Year
DOI
Venue
2017
10.1093/bioinformatics/btw804
BIOINFORMATICS
Field
DocType
Volume
Data science,Data structure,Data mining,Association tests,Covariate,Computer science,Microbiome,Genomics,Genetic association,R package
Journal
33
Issue
ISSN
Citations 
9
1367-4803
0
PageRank 
References 
Authors
0.34
1
4
Name
Order
Citations
PageRank
Zheng-Zheng Tang110.69
Guanhua Chen2217.78
Alexander V. Alekseyenko3379.10
Hong-Zhe Li450866.31