Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection

Since the turn of the century, technological advances have made it possible to obtain the molecular profile of any tissue in a cost-effective manner. Among these advances are sophisticated high-throughput assays that measure the relative abundances of microorganisms, RNA molecules, and metabolites....

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Detalles Bibliográficos
Autores: Quinn, Thomas P., Erb, Ionas
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/44491
Acceso en línea:http://hdl.handle.net/10230/44491
http://dx.doi.org/10.1128/mSystems.00230-19
Access Level:acceso abierto
Palabra clave:Balances
Classification
Coda
Compositional data
Log contrast
Log ratio
Machine learning
Microbiome
Prediction
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spelling Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selectionQuinn, Thomas P.Erb, IonasBalancesClassificationCodaCompositional dataLog contrastLog ratioMachine learningMicrobiomePredictionSince the turn of the century, technological advances have made it possible to obtain the molecular profile of any tissue in a cost-effective manner. Among these advances are sophisticated high-throughput assays that measure the relative abundances of microorganisms, RNA molecules, and metabolites. While these data are most often collected to gain new insights into biological systems, they can also be used as biomarkers to create clinically useful diagnostic classifiers. How best to classify high-dimensional -omics data remains an area of active research. However, few explicitly model the relative nature of these data and instead rely on cumbersome normalizations. This report (i) emphasizes the relative nature of health biomarkers, (ii) discusses the literature surrounding the classification of relative data, and (iii) benchmarks how different transformations perform for regularized logistic regression across multiple biomarker types. We show how an interpretable set of log contrasts, called balances, can prepare data for classification. We propose a simple procedure, called discriminative balance analysis, to select groups of 2 and 3 bacteria that can together discriminate between experimental conditions. Discriminative balance analysis is a fast, accurate, and interpretable alternative to data normalization.IMPORTANCE High-throughput sequencing provides an easy and cost-effective way to measure the relative abundance of bacteria in any environmental or biological sample. When these samples come from humans, the microbiome signatures can act as biomarkers for disease prediction. However, because bacterial abundance is measured as a composition, the data have unique properties that make conventional analyses inappropriate. To overcome this, analysts often use cumbersome normalizations. This article proposes an alternative method that identifies pairs and trios of bacteria whose stoichiometric presence can differentiate between diseased and nondiseased samples. By using interpretable log contrasts called balances, we developed an entirely normalization-free classification procedure that reduces the feature space and improves the interpretability, without sacrificing classifier performance.American Society for Microbiology202020202020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/44491http://dx.doi.org/10.1128/mSystems.00230-19reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésmSystems. 2020; 5(2). pii: e00230-19© 2020 Quinn and Erb. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/).https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/444912026-06-12T07:21:37Z
dc.title.none.fl_str_mv Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
title Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
spellingShingle Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
Quinn, Thomas P.
Balances
Classification
Coda
Compositional data
Log contrast
Log ratio
Machine learning
Microbiome
Prediction
title_short Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
title_full Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
title_fullStr Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
title_full_unstemmed Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
title_sort Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
dc.creator.none.fl_str_mv Quinn, Thomas P.
Erb, Ionas
author Quinn, Thomas P.
author_facet Quinn, Thomas P.
Erb, Ionas
author_role author
author2 Erb, Ionas
author2_role author
dc.subject.none.fl_str_mv Balances
Classification
Coda
Compositional data
Log contrast
Log ratio
Machine learning
Microbiome
Prediction
topic Balances
Classification
Coda
Compositional data
Log contrast
Log ratio
Machine learning
Microbiome
Prediction
description Since the turn of the century, technological advances have made it possible to obtain the molecular profile of any tissue in a cost-effective manner. Among these advances are sophisticated high-throughput assays that measure the relative abundances of microorganisms, RNA molecules, and metabolites. While these data are most often collected to gain new insights into biological systems, they can also be used as biomarkers to create clinically useful diagnostic classifiers. How best to classify high-dimensional -omics data remains an area of active research. However, few explicitly model the relative nature of these data and instead rely on cumbersome normalizations. This report (i) emphasizes the relative nature of health biomarkers, (ii) discusses the literature surrounding the classification of relative data, and (iii) benchmarks how different transformations perform for regularized logistic regression across multiple biomarker types. We show how an interpretable set of log contrasts, called balances, can prepare data for classification. We propose a simple procedure, called discriminative balance analysis, to select groups of 2 and 3 bacteria that can together discriminate between experimental conditions. Discriminative balance analysis is a fast, accurate, and interpretable alternative to data normalization.IMPORTANCE High-throughput sequencing provides an easy and cost-effective way to measure the relative abundance of bacteria in any environmental or biological sample. When these samples come from humans, the microbiome signatures can act as biomarkers for disease prediction. However, because bacterial abundance is measured as a composition, the data have unique properties that make conventional analyses inappropriate. To overcome this, analysts often use cumbersome normalizations. This article proposes an alternative method that identifies pairs and trios of bacteria whose stoichiometric presence can differentiate between diseased and nondiseased samples. By using interpretable log contrasts called balances, we developed an entirely normalization-free classification procedure that reduces the feature space and improves the interpretability, without sacrificing classifier performance.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
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/10230/44491
http://dx.doi.org/10.1128/mSystems.00230-19
url http://hdl.handle.net/10230/44491
http://dx.doi.org/10.1128/mSystems.00230-19
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv mSystems. 2020; 5(2). pii: e00230-19
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Society for Microbiology
publisher.none.fl_str_mv American Society for Microbiology
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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