Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7914
Title: Deciphering associations between gut microbiota and clinical factors using microbial modules
Authors: Wang, Ran 
Zheng, Xubin 
Song, Fangda 
Wong. Man Hon 
Prof. LEUNG Kwong Sak 
Cheng, Lixin 
Kendziorski, Christina 
Issue Date: 2023
Source: Bioinformatics, May 2023, vol. 39(5), article no. btad213.
Journal: Bioinformatics 
Abstract: Motivation Human gut microbiota plays a vital role in maintaining body health. The dysbiosis of gut microbiota is associated with a variety of diseases. It is critical to uncover the associations between gut microbiota and disease states as well as other intrinsic or environmental factors. However, inferring alterations of individual microbial taxa based on relative abundance data likely leads to false associations and conflicting discoveries in different studies. Moreover, the effects of underlying factors and microbe–microbe interactions could lead to the alteration of larger sets of taxa. It might be more robust to investigate gut microbiota using groups of related taxa instead of the composition of individual taxa. Results We proposed a novel method to identify underlying microbial modules, i.e. groups of taxa with similar abundance patterns affected by a common latent factor, from longitudinal gut microbiota and applied it to inflammatory bowel disease (IBD). The identified modules demonstrated closer intragroup relationships, indicating potential microbe–microbe interactions and influences of underlying factors. Associations between the modules and several clinical factors were investigated, especially disease states. The IBD-associated modules performed better in stratifying the subjects compared with the relative abundance of individual taxa. The modules were further validated in external cohorts, demonstrating the efficacy of the proposed method in identifying general and robust microbial modules. The study reveals the benefit of considering the ecological effects in gut microbiota analysis and the great promise of linking clinical factors with underlying microbial modules.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7914
ISSN: 1367-4803
1367-4811
DOI: 10.1093/bioinformatics/btad213
Appears in Collections:Applied Data Science - Publication

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