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ENIGMA: an enterotype-like unigram mixture model for microbial association analysis
Ko Abe1, Masaaki Hirayama2, Kinji Ohno3
1Division of Systems Biology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan.
BMC Genomics
|April 11, 2019
Summary
A new model, ENIGMA, distinguishes disease-related gut bacteria from enterotypes. This microbial association analysis accurately identifies bacteria linked to Parkinson's disease using real-world data.
Area of Science:
- Microbiology
- Computational Biology
- Statistical Genetics
Background:
- Microbiome studies face challenges in linking microbial communities to specific diseases.
- Intestinal bacterial communities exhibit enterotypes, complicating disease association analyses.
- Traditional models struggle to differentiate enterotype-driven variations from disease-specific microbial changes.
Purpose of the Study:
- To introduce ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), a novel probabilistic model.
- To enable simultaneous estimation of enterotype clusters and disease-associated environmental effects.
- To improve the analysis of microbiome data in health and disease contexts.
Main Methods:
- Development of the ENIGMA probabilistic model.
- Simulations to evaluate the accuracy of parameter estimation.
- Application of ENIGMA to real-world microbiome data.
Main Results:
- ENIGMA successfully estimates enterotype-like clusters based on bacterial abundances.
- The model allows for the simultaneous estimation of disease-related parameters.
- Analysis of real-world data identified specific bacteria associated with Parkinson's disease.
Conclusions:
- ENIGMA provides an accurate method for parameter estimation in microbiome studies.
- The model successfully detected bacteria related to Parkinson's disease.
- ENIGMA is implemented in R and publicly available on GitHub.
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