Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation

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• dc.contributor.author Greenacre, Michael
• dc.contributor.author Martínez-Álvaro, Marina
• dc.contributor.author Blasco, Agustín
• dc.date.accessioned 2023-06-15T06:02:40Z
• dc.date.available 2023-06-15T06:02:40Z
• dc.date.issued 2021
• dc.description.abstract Microbiome and omics datasets are, by their intrinsic biological nature, of high dimensionality, characterized by counts of large numbers of components (microbial genes, operational taxonomic units, RNA transcripts, etc.). These data are generally regarded as compositional since the total number of counts identified within a sample is irrelevant. The central concept in compositional data analysis is the logratio transformation, the simplest being the additive logratios with respect to a fixed reference component. A full set of additive logratios is not isometric, that is they do not reproduce the geometry of all pairwise logratios exactly, but their lack of isometry can be measured by the Procrustes correlation. The reference component can be chosen to maximize the Procrustes correlation between the additive logratio geometry and the exact logratio geometry, and for high-dimensional data there are many potential references. As a secondary criterion, minimizing the variance of the reference component's log-transformed relative abundance values makes the subsequent interpretation of the logratios even easier. On each of three high-dimensional omics datasets the additive logratio transformation was performed, using references that were identified according to the abovementioned criteria. For each dataset the compositional data structure was successfully reproduced, that is the additive logratios were very close to being isometric. The Procrustes correlations achieved for these datasets were 0.9991, 0.9974, and 0.9902, respectively. We thus demonstrate, for high-dimensional compositional data, that additive logratios can provide a valid choice as transformed variables, which (a) are subcompositionally coherent, (b) explain 100% of the total logratio variance and (c) come measurably very close to being isometric. The interpretation of additive logratios is much simpler than the complex isometric alternatives and, when the variance of the log-transformed reference is very low, it is even simpler since each additive logratio can be identified with a corresponding compositional component.
• dc.description.sponsorship Support is acknowledged from the Spanish National Plan of Scientific Research, Project PID2020-115558GB-C21.
• dc.format.mimetype application/pdf
• dc.identifier.citation Greenacre M, Martínez-Álvaro M, Blasco A. Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation. Front Microbiol. 2021;12:727398. DOI: 10.3389/fmicb.2021.727398
• dc.identifier.doi http://dx.doi.org/10.3389/fmicb.2021.727398
• dc.identifier.issn 1664-302X
• dc.identifier.uri http://hdl.handle.net/10230/57178
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Microbiology. 2021;12:727398.
• dc.relation.isreferencedby https://www.frontiersin.org/articles/10.3389/fmicb.2021.727398/full#supplementary-material
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-115558GB-C21
• dc.rights © 2021 Greenacre, Martínez-Álvaro and Blasco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword compositional data
• dc.subject.keyword dimension reduction
• dc.subject.keyword logratio transformation
• dc.subject.keyword logratio geometry
• dc.subject.keyword logratio variance
• dc.subject.keyword Procrustes correlation
• dc.subject.keyword variable selection
• dc.title Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion