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

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...

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Authors: Greenacre, Michael, Martínez-Álvaro, Marina, Blasco, Agustín
Format: article
Status:Published version
Publication Date:2021
Country:España
Institution:Universitat Pompeu Fabra
Repository:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/57178
Online Access:http://hdl.handle.net/10230/57178
http://dx.doi.org/10.3389/fmicb.2021.727398
Access Level:Open access
Keyword:compositional data
dimension reduction
logratio transformation
logratio geometry
logratio variance
Procrustes correlation
variable selection
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spelling Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformationGreenacre, MichaelMartínez-Álvaro, MarinaBlasco, Agustíncompositional datadimension reductionlogratio transformationlogratio geometrylogratio varianceProcrustes correlationvariable selectionMicrobiome 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.Support is acknowledged from the Spanish National Plan of Scientific Research, Project PID2020-115558GB-C21.Frontiers202320232021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/57178http://dx.doi.org/10.3389/fmicb.2021.727398reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésFrontiers in Microbiology. 2021;12:727398.https://www.frontiersin.org/articles/10.3389/fmicb.2021.727398/full#supplementary-materialinfo:eu-repo/grantAgreement/ES/2PE/PID2020-115558GB-C21© 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.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/571782026-06-12T07:21:37Z
dc.title.none.fl_str_mv Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
title Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
spellingShingle Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
Greenacre, Michael
compositional data
dimension reduction
logratio transformation
logratio geometry
logratio variance
Procrustes correlation
variable selection
title_short Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
title_full Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
title_fullStr Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
title_full_unstemmed Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
title_sort Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation
dc.creator.none.fl_str_mv Greenacre, Michael
Martínez-Álvaro, Marina
Blasco, Agustín
author Greenacre, Michael
author_facet Greenacre, Michael
Martínez-Álvaro, Marina
Blasco, Agustín
author_role author
author2 Martínez-Álvaro, Marina
Blasco, Agustín
author2_role author
author
dc.subject.none.fl_str_mv compositional data
dimension reduction
logratio transformation
logratio geometry
logratio variance
Procrustes correlation
variable selection
topic compositional data
dimension reduction
logratio transformation
logratio geometry
logratio variance
Procrustes correlation
variable selection
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/57178
http://dx.doi.org/10.3389/fmicb.2021.727398
url http://hdl.handle.net/10230/57178
http://dx.doi.org/10.3389/fmicb.2021.727398
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Frontiers in Microbiology. 2021;12:727398.
https://www.frontiersin.org/articles/10.3389/fmicb.2021.727398/full#supplementary-material
info:eu-repo/grantAgreement/ES/2PE/PID2020-115558GB-C21
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
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application/pdf
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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