miMatch: a microbial metabolic background matching tool for mitigating host confounding in metagenomics research

Lei Liu; Suqi Cao; Weili Lin; Zhigang Gao; Liu Yang; Lixin Zhu; Bin Yang; Guoqing Zhang; Ruixin Zhu; Dingfeng Wu

doi:10.1080/19490976.2024.2434029

Gut Microbes (Dec 2024)

miMatch: a microbial metabolic background matching tool for mitigating host confounding in metagenomics research

Lei Liu,
Suqi Cao,
Weili Lin,
Zhigang Gao,
Liu Yang,
Lixin Zhu,
Bin Yang,
Guoqing Zhang,
Ruixin Zhu,
Dingfeng Wu

Affiliations

Lei Liu: Department of Gastroenterology, The Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
Suqi Cao: National Center, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, P. R. China
Weili Lin: Department of Gastroenterology, The Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
Zhigang Gao: Department of General Surgery, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, P. R. China
Liu Yang: National Center, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, P. R. China
Lixin Zhu: Guangdong Institute of Gastroenterology; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases; Biomedical Innovation Center, Sun Yat-Sen University, Guangzhou, P. R. China
Bin Yang: Shanghai Southgene Technology Co., Ltd., Shanghai, China
Guoqing Zhang: National Genomics Data Center & Bio-Med Big Data Center, Chinese Academy of Sciences Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Shanghai, P. R. China
Ruixin Zhu: Department of Gastroenterology, The Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, P. R. China
Dingfeng Wu: National Center, Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, P. R. China

DOI: https://doi.org/10.1080/19490976.2024.2434029
Journal volume & issue: Vol. 16, no. 1

Abstract

Read online

Metagenomic research faces a persistent challenge due to the low concordance across studies. While matching host confounders can mitigate the impact of individual differences, the influence of factors such as genetics, environment, and lifestyle habits on microbial profiles makes it exceptionally challenging to create fully matched cohorts. The microbial metabolic background, which modulates microbial composition, reflects a cumulative impact of host confounders, serving as an ideal baseline for microbial sample matching. In this study, we introduced miMatch, an innovative metagenomic sample-matching tool that uses microbial metabolic background as a comprehensive reference for host-related variables and employs propensity score matching to build case-control pairs, even in the absence of host confounders. In the simulated datasets, miMatch effectively eliminated individual metabolic background differences, thereby enhancing the accuracy of identifying differential microbial patterns and reducing false positives. Moreover, in real metagenomic data, miMatch improved result consistency and model generalizability across cohorts of the same disease. A user-friendly web server (https://www.biosino.org/iMAC/mimatch) has been established to promote the integration of multiple metagenomic cohorts, strengthening causal relationships in metagenomic research.

Published in Gut Microbes

ISSN: 1949-0976 (Print); 1949-0984 (Online)
Publisher: Taylor & Francis Group
Country of publisher: United States
LCC subjects: Medicine: Internal medicine: Specialties of internal medicine: Diseases of the digestive system. Gastroenterology
Website: https://www.tandfonline.com/journals/kgmi

About the journal

Abstract

Keywords