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Melody: meta-analysis of microbiome association studies for discovering generalizable microbial signatures
Zhoujingpeng Wei1, Guanhua Chen1, Zheng-Zheng Tang2
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53715, USA.
Abstract:
Standard protocols for meta-analysis of association studies are inadequate for microbiome data due to their complex compositional structure, leading to inaccurate and unstable microbial signature selection. To address this issue, we introduce Melody, a framework that generates, harmonizes, and combines study-specific summary association statistics to powerfully and robustly identify microbial signatures in meta-analysis. Comprehensive and realistic simulations demonstrate that Melody substantially outperforms existing approaches in prioritizing true signatures. In the meta-analyses of five studies on colorectal cancer and eight studies on the gut metabolome, we showcase the superior stability, reliability, and predictive performance of Melody-identified signatures.
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