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

Publishing date: 2020-01-15

Published on: PLOS ONE

summary: Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest such as disease state. A key challenge in this is the composition and dimensionality of microbiome data. Additionally, 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. Here, the authors provide an approach to explore co-occurring taxa using “topics” generated via a topic model and apply their method to three publicly available 16S amplicon sequencing datasets.

authors: Stephen Woloszynek, Joshua Chang Mell, Zhengqiao Zhao, Gideon Simpson, Michael P. O’Connor, Gail L. Rosen

link to paper: 10.1371/journal.pone.0219235

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