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

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Microbiome data analysis is challenging due to its compositional, sparse, and high-dimensional nature.
• Identifying co-occurring taxa and their functional roles is crucial for understanding host-microbe interactions and disease states.
• Current methods often aggregate functional predictions, obscuring the contributions of specific microbial subcommunities.

Purpose of the Study:

• To develop a novel topic modeling approach for exploring co-occurring taxa in microbiome data.
• To link identified microbial 'topics' to specific sample features, such as disease status.
• To infer functional potential within these microbial topics and their association with host or environmental factors.

Main Methods:

• Utilized a topic model to group co-occurring taxa into 'topics'.
• Employed a multilevel, fully Bayesian regression model to estimate interactions between topics and biological pathways.
• Applied the methods to three public 16S rRNA amplicon sequencing datasets (IBD, oral cancer, time-series).

Main Results:

• Successfully captured groups of co-occurring taxa (topics) within 16S rRNA amplicon surveys.
• Enabled the uncovering of within-topic functional potential and linked it to sample features.
• Demonstrated the ability to link microbial co-occurrence patterns with gene function and host characteristics.

Conclusions:

• The topic model approach provides a robust framework for analyzing complex microbiome data.
• It facilitates a deeper understanding of microbial community structure, function, and their relationship with host status.
• The developed methods are implemented in an accessible R package for broader scientific use.