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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. and Zych, K. and Essex, M. and Karcher, N. and Kartal, E. and Salazar, G. and Bork, P. and 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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