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.

中文翻译：

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miMatch：一种微生物代谢背景匹配工具，用于减轻宏基因组学研究中的宿主混杂。


由于研究之间的一致性低，宏基因组研究面临着持续的挑战。虽然匹配的宿主混杂因素可以减轻个体差异的影响，但遗传、环境和生活习惯等因素对微生物特征的影响使得创建完全匹配的队列变得异常具有挑战性。调节微生物组成的微生物代谢背景反映了宿主混杂因素的累积影响，是微生物样品匹配的理想基线。在这项研究中，我们引入了 miMatch，这是一种创新的宏基因组样本匹配工具，它使用微生物代谢背景作为宿主相关变量的综合参考，并采用倾向评分匹配来构建病例对照对，即使在没有宿主混杂因素的情况下也是如此。在模拟数据集中，miMatch 有效地消除了个体代谢背景差异，从而提高了识别差异微生物模式的准确性，减少了假阳性。此外，在真实的宏基因组数据中，miMatch 提高了同一疾病队列之间的结果一致性和模型泛化性。已经建立了用户友好的 Web 服务器 （https://www.biosino.org/iMAC/mimatch） 以促进多个宏基因组队列的整合，加强宏基因组研究中的因果关系。