Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox
Wirbel, Jakob | Zych, Konrad | Essex, Morgan | Karcher, Nicolai | Kartal, Ece | Salazar, Guillem | Bork, Peer | Sunagawa, Shinichi | Zeller, Georg
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .
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