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dc.contributor.authorSusín Sánchez, Antonio
dc.contributor.authorCalle, M. L.
dc.contributor.authorWang, Yiwen
dc.contributor.authorLe Cao, Kim-Anh
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2021-02-22T12:41:54Z
dc.date.available2021-02-22T12:41:54Z
dc.date.issued2020-06-01
dc.identifier.citationSusin, A. [et al.]. Variable selection in microbiome compositional data analysis. "NAR Genomics and Bioinformatics", 1 Juny 2020, vol. 2, núm. 2, p. lqaa0297/1-lqaa029/14.
dc.identifier.issn2631-9268
dc.identifier.urihttp://hdl.handle.net/2117/340287
dc.description.abstractThough variable selection is one of the most relevant tasks in microbiome analysis, e.g. for the identification of microbial signatures, many studies still rely on methods that ignore the compositional nature of microbiome data. The applicability of compositional data analysis methods has been hampered by the availability of software and the difficulty in interpreting their results. This work is focused on three methods for variable selection that acknowledge the compositional structure of microbiome data: selbal, a forward selection approach for the identification of compositional balances, and clr-lasso and codalasso, two penalized regression models for compositional data analysis. This study highlights the link between these methods and brings out some limitations of the centered log-ratio transformation for variable selection. In particular, the fact that it is not subcompositionally consistent makes the microbial signatures obtained from clr-lasso not readily transferable. Coda-lasso is computationally efficient and suitable when the focus is the identification of the most associated microbial taxa. Selbal stands out when the goal is to obtain a parsimonious model with optimal prediction performance, but it is computationally greedy. We provide a reproducible vignette for the application of these methods that will enable researchers to fully leverage their potential in microbiome studies.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut
dc.subject.lcshMicrobiology
dc.titleVariable selection in microbiome compositional data analysis
dc.typeArticle
dc.subject.lemacMicrobiologia
dc.subject.lemacADN -- Estructura
dc.contributor.groupUniversitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica
dc.identifier.doi10.1093/nargab/lqaa029
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://academic.oup.com/nargab/article/2/2/lqaa029/5836692
dc.rights.accessOpen Access
local.identifier.drac30364042
dc.description.versionPostprint (published version)
local.citation.authorSusin, A.; Calle, M.; Wang, Y.; Le Cao, K.
local.citation.publicationNameNAR Genomics and Bioinformatics
local.citation.volume2
local.citation.number2
local.citation.startingPagelqaa0297/1
local.citation.endingPagelqaa029/14


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain