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Utility of multivariate data analysis and penalized meta-regression to explore sources of heterogeneity in microbiome meta-analyses

hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Arthur
hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierMéthodes avancées d’apprentissage statistique et de contrôle [ASTRAL]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
hal.structure.identifierUniversité de Bordeaux [UB]
dc.contributor.authorAVALOS, Marta
dc.date.accessioned2024-04-04T02:32:56Z
dc.date.available2024-04-04T02:32:56Z
dc.date.conference2023-10-26
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190430
dc.description.abstractEnMeta-analysis is a statistical method that quantitatively synthesizes, by calculating a combined result, the results of independent studies addressing a specific research question. The principle is simple: pooling data from several studies increases statistical power. However, a number of conditions must be assessed to ensure that the combined result is not biased and that the conclusions drawn are accurate. A key step is to explore the sources of heterogeneity and look for possible biases. Advances in bioinformatics and next-generation sequencing have led to important advances in the understanding of the role of the microbiota in health. Knowledge development is often based on the conclusions that can be drawn from all published data, i.e. meta-analyses. Yet, in microbiota studies, differences between studies (in terms of pipelines, characteristics, sequencing techniques, samplecollection sites, study populations, etc.) can be very high. An exploration of the sources of heterogeneity is essential to determine whether studies, even if they address the same research question, are comparable.Multivariate data analysis methods (such as principal components analysis for quantitative data, multiple correspondence analysis for categorical data, and factor analysis of mixed data, a mixture of the two) as well as penalized meta-regression (such as the Lasso) are applied to explore heterogeneity in microbiota meta-analyses. Data from recently published microbiome meta-analyses were re-analyzed with the developed tools. In this work, we illustrate the utility of multivariate data analysis methods and penalized meta-regression in exploring sources of heterogeneity in microbiota meta-analyses.
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.enMicrobiome meta-analyses
dc.subject.enHeterogeneity
dc.subject.enVisual tools for data exploration
dc.subject.enMeta-regression
dc.title.enUtility of multivariate data analysis and penalized meta-regression to explore sources of heterogeneity in microbiome meta-analyses
dc.typeAutre communication scientifique (congrès sans actes - poster - séminaire...)
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologie
dc.subject.halStatistiques [stat]/Applications [stat.AP]
dc.subject.halStatistiques [stat]/Méthodologie [stat.ME]
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleWoM 2023 - 4th International World of Microbiome Conference
bordeaux.countryBG
bordeaux.conference.citySofia
bordeaux.peerReviewedoui
hal.identifierhal-04260888
hal.version1
hal.proceedingsnon
hal.conference.end2023-10-28
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04260888v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=SARACCO,%20Arthur&CHAVENT,%20Marie&AVALOS,%20Marta&rft.genre=conference


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