ENIGMA: an enterotype-like unigram mixture model for microbial association analysis

BMC Genomics

Kinji Ohno¹  Ko Abe²  Teppei Shimamura²  Masaaki Hirayama³ 

[1] Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine;Division of Systems Biology, Nagoya University Graduate School of Medicine;School of Health Sciences, Nagoya University Graduate School of Medicine;

关键词: Enterotype;    Topic model;    Unigram mixture;    Bayesian inference;    Metagenomics;   

DOI  :  10.1186/s12864-019-5476-9

来源: DOAJ

【 摘 要 】

Abstract Background One of the major challenges in microbial studies is detecting associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities form clusters called as “enterotype”, which are characterized by differences in specific bacterial taxa, making it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish between the bacterial differences derived from enterotype and those related to a specific disease. Results We propose a new probabilistic model, named as ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), which can be used to address these problems. ENIGMA enabled simultaneous estimation of enterotype-like clusters characterized by the abundances of signature bacterial genera and the parameters of environmental effects associated with the disease. Conclusion In the simulation study, we evaluated the accuracy of parameter estimation. Furthermore, by analyzing the real-world data, we detected the bacteria related to Parkinson’s disease. ENIGMA is implemented in R and is available from GitHub (https://github.com/abikoushi/enigma).

【 授权许可】

Unknown