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Papoutsoglou G, Tarazona S, Lopes MB, Klammsteiner T, Ibrahimi E, Eckenberger J, Novielli P, Tonda A, Simeon A, Shigdel R, Béreux S, Vitali G, Tangaro S, Lahti L, Temko A, Claesson MJ, Berland M. Machine learning approaches in microbiome research: challenges and best practices. Front Microbiol 2023; 14:1261889. [PMID: 37808286 PMCID: PMC10556866 DOI: 10.3389/fmicb.2023.1261889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 09/04/2023] [Indexed: 10/10/2023] Open
Abstract
Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.
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Affiliation(s)
- Georgios Papoutsoglou
- Department of Computer Science, University of Crete, Heraklion, Greece
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, Heraklion, Greece
| | - Sonia Tarazona
- Department of Applied Statistics and Operations Research and Quality, Polytechnic University of Valencia, Valencia, Spain
| | - Marta B. Lopes
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- Research and Development Unit for Mechanical and Industrial Engineering (UNIDEMI), Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal
| | - Thomas Klammsteiner
- Department of Ecology, Universität Innsbruck, Innsbruck, Austria
- Department of Microbiology, Universität Innsbruck, Innsbruck, Austria
| | - Eliana Ibrahimi
- Department of Biology, University of Tirana, Tirana, Albania
| | - Julia Eckenberger
- School of Microbiology, University College Cork, Cork, Ireland
- APC Microbiome Ireland, Cork, Ireland
| | - Pierfrancesco Novielli
- Department of Soil, Plant, and Food Sciences, University of Bari Aldo Moro, Bari, Italy
- National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - Alberto Tonda
- UMR 518 MIA-PS, INRAE, Paris-Saclay University, Palaiseau, France
- Complex Systems Institute of Paris Ile-de-France (ISC-PIF) - UAR 3611 CNRS, Paris, France
| | - Andrea Simeon
- BioSense Institute, University of Novi Sad, Novi Sad, Serbia
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Stéphane Béreux
- MetaGenoPolis, INRAE, Paris-Saclay University, Jouy-en-Josas, France
- MaIAGE, INRAE, Paris-Saclay University, Jouy-en-Josas, France
| | - Giacomo Vitali
- MetaGenoPolis, INRAE, Paris-Saclay University, Jouy-en-Josas, France
| | - Sabina Tangaro
- Department of Soil, Plant, and Food Sciences, University of Bari Aldo Moro, Bari, Italy
- National Institute for Nuclear Physics, Bari Division, Bari, Italy
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Andriy Temko
- Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | - Marcus J. Claesson
- School of Microbiology, University College Cork, Cork, Ireland
- APC Microbiome Ireland, Cork, Ireland
| | - Magali Berland
- MetaGenoPolis, INRAE, Paris-Saclay University, Jouy-en-Josas, France
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Coenders G, Greenacre M. Three approaches to supervised learning for compositional data with pairwise logratios. J Appl Stat 2022; 50:3272-3293. [PMID: 37969895 PMCID: PMC10637191 DOI: 10.1080/02664763.2022.2108007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/25/2022] [Indexed: 10/15/2022]
Abstract
Logratios between pairs of compositional parts (pairwise logratios) are the easiest to interpret in compositional data analysis, and include the well-known additive logratios as particular cases. When the number of parts is large (sometimes even larger than the number of cases), some form of logratio selection is needed. In this article, we present three alternative stepwise supervised learning methods to select the pairwise logratios that best explain a dependent variable in a generalized linear model, each geared for a specific problem. The first method features unrestricted search, where any pairwise logratio can be selected. This method has a complex interpretation if some pairs of parts in the logratios overlap, but it leads to the most accurate predictions. The second method restricts parts to occur only once, which makes the corresponding logratios intuitively interpretable. The third method uses additive logratios, so that K-1 selected logratios involve a K-part subcomposition. Our approach allows logratios or non-compositional covariates to be forced into the models based on theoretical knowledge, and various stopping criteria are available based on information measures or statistical significance with the Bonferroni correction. We present an application on a dataset from a study predicting Crohn's disease.
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Affiliation(s)
- Germà Coenders
- Department of Economics, Universitat de Girona, Girona, Spain
| | - Michael Greenacre
- Department of Economics and Business and Barcelona School of Management, Universitat Pompeu Fabra, Barcelona, Spain
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