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dc.contributor.authorIbrahimi, Eliana
dc.contributor.authorLopes, Marta B.
dc.contributor.authorDhamo, Xhilda
dc.contributor.authorSimeon, Andrea
dc.contributor.authorShigdel, Rajesh
dc.contributor.authorHron, Karel
dc.contributor.authorStres, Blaž
dc.contributor.authorD’Elia, Domenica
dc.contributor.authorBerland, Magali
dc.contributor.authorMarcos-Zambrano, Laura Judith
dc.date.accessioned2024-08-01T09:00:08Z
dc.date.available2024-08-01T09:00:08Z
dc.date.created2023-11-15T09:43:35Z
dc.date.issued2023
dc.identifier.issn1664-302X
dc.identifier.urihttps://hdl.handle.net/11250/3144012
dc.description.abstractAlthough metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.en_US
dc.language.isoengen_US
dc.publisherFrontiersen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleOverview of data preprocessing for machine learning applications in human microbiome researchen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.source.articlenumber1250909en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2
dc.identifier.doi10.3389/fmicb.2023.1250909
dc.identifier.cristin2196853
dc.source.journalFrontiers in Microbiologyen_US
dc.identifier.citationFrontiers in Microbiology. 2023, 14, 1250909.en_US
dc.source.volume14en_US


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