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

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dc.contributor.author Quinn, Thomas P.
dc.contributor.author Erb, Ionas
dc.date.accessioned 2020-05-12T07:10:04Z
dc.date.available 2020-05-12T07:10:04Z
dc.date.issued 2020
dc.identifier.citation Quinn TP, Erb I. Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection. mSystems. 2020; 5(2). pii: e00230-19. DOI: 10.1128/mSystems.00230-19
dc.identifier.issn 2379-5077
dc.identifier.uri http://hdl.handle.net/10230/44491
dc.description.abstract 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher American Society for Microbiology
dc.relation.ispartof mSystems. 2020; 5(2). pii: e00230-19
dc.rights © 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/).
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Interpretable log contrasts for the classification of health biomarkers: a new approach to balance selection
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1128/mSystems.00230-19
dc.subject.keyword Balances
dc.subject.keyword Classification
dc.subject.keyword Coda
dc.subject.keyword Compositional data
dc.subject.keyword Log contrast
dc.subject.keyword Log ratio
dc.subject.keyword Machine learning
dc.subject.keyword Microbiome
dc.subject.keyword Prediction
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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