Exploring thematic structure and predicted functionality of 16S rRNA amplicon data

Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occu...

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Veröffentlicht in:PloS one 2019-12, Vol.14 (12), p.e0219235
Hauptverfasser: Woloszynek, Stephen, Mell, Joshua Chang, Zhao, Zhengqiao, Simpson, Gideon, O'Connor, Michael P, Rosen, Gail L
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container_start_page e0219235
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creator Woloszynek, Stephen
Mell, Joshua Chang
Zhao, Zhengqiao
Simpson, Gideon
O'Connor, Michael P
Rosen, Gail L
description Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using "topics" generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset, an oral cancer dataset, and a time-series dataset. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://cran.r-project.org/package=themetagenomics, https://github.com/EESI/themetagenomics.
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subjects Bacteria - classification
Bacteria - genetics
Bayesian analysis
Biodiversity
Biological indicators
Biology and Life Sciences
Computer and Information Sciences
Computer engineering
Configurations
Crohn Disease - microbiology
Data analysis
Datasets
Functionals
Gastrointestinal diseases
Genes
Genetic research
Genomes
Humans
Indicator species
Inflammatory bowel diseases
Intestine
Medicine and Health Sciences
Metagenomics - methods
Methods
Microbiomes
Microbiota
Microorganisms
Mouth cancer
Mouth Neoplasms - microbiology
Natural language processing
Oral cancer
Phylogeny
Regression analysis
Regression models
Research and analysis methods
RNA
RNA, Ribosomal, 16S - genetics
rRNA 16S
Sequence Analysis, DNA
Sequences
Software
Taxa
Taxonomy
Time Factors
title Exploring thematic structure and predicted functionality of 16S rRNA amplicon data
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