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| Item Type: | Article |
|---|---|
| Title: | Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox |
| Creators: |
Wirbel, J.  ORCID: https://orcid.org/0000-0002-4073-3562, Zych, K. ORCID: https://orcid.org/0000-0001-7426-0516, Essex, M. ORCID: https://orcid.org/0000-0001-8758-7497, Karcher, N. ORCID: https://orcid.org/0000-0001-7894-8182, Kartal, E. ORCID: https://orcid.org/0000-0002-7720-455X, Salazar, G. ORCID: https://orcid.org/0000-0002-9786-1493, Bork, P. ORCID: https://orcid.org/0000-0002-2627-833X, Sunagawa, S. ORCID: https://orcid.org/0000-0003-3065-0314 and Zeller, G. ORCID: https://orcid.org/0000-0003-1429-7485
|
| 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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