Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox

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Item Type:	Article
Title:	Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox
Creators Name:	Wirbel, J., Zych, K., Essex, M., Karcher, N., Kartal, E., Salazar, G., Bork, P., Sunagawa, S. and Zeller, G.
Abstract:	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.
Keywords:	Microbiome Data Analysis, Machine Learning, Statistical Modeling, Microbiome-Wide Association Studies (MWAS), Meta-Analysis
Source:	Genome Biology
ISSN:	1474-760X
Publisher:	BioMed Central
Volume:	22
Number:	1
Page Range:	93
Date:	30 March 2021
Official Publication:	https://doi.org/10.1186/s13059-021-02306-1
PubMed:	View item in PubMed

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