How should we measure proportionality on relative gene expression data?

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• dc.contributor.author Erb, Ionasca
• dc.contributor.author Notredame, Cedricca
• dc.date.accessioned 2017-04-26T10:07:22Z
• dc.date.available 2017-04-26T10:07:22Z
• dc.date.issued 2016
• dc.description.abstract Correlation is ubiquitously used in gene expression analysis although its validity as an objective criterion is often questionable. If no normalization reflecting the original mRNA counts in the cells is available, correlation between genes becomes spurious. Yet the need for normalization can be bypassed using a relative analysis approach called log-ratio analysis. This approach can be used to identify proportional gene pairs, i.e. a subset of pairs whose correlation can be inferred correctly from unnormalized data due to their vanishing log-ratio variance. To interpret the size of non-zero log-ratio variances, a proposal for a scaling with respect to the variance of one member of the gene pair was recently made by Lovell et al. Here we derive analytically how spurious proportionality is introduced when using a scaling. We base our analysis on a symmetric proportionality coefficient (briefly mentioned in Lovell et al.) that has a number of advantages over their statistic. We show in detail how the choice of reference needed for the scaling determines which gene pairs are identified as proportional. We demonstrate that using an unchanged gene as a reference has huge advantages in terms of sensitivity. We also explore the link between proportionality and partial correlation and derive expressions for a partial proportionality coefficient. A brief data-analysis part puts the discussed concepts into practice.
• dc.description.sponsorship The authors were supported by CRG internal funds provided by the Catalan Government. I. E. was also paid by the Spanish Ministerio de Economía y Competitividad under Grant BFU2011-28575.
• dc.format.mimetype application/pdfca
• dc.identifier.citation Erb I, Notredame C. How should we measure proportionality on relative gene expression data? Theory in Biosciences. 2016;135(1):21-36. DOI: 10.1007/s12064-015-0220-8
• dc.identifier.doi http://dx.doi.org/10.1007/s12064-015-0220-8
• dc.identifier.issn 1431-7613
• dc.identifier.uri http://hdl.handle.net/10230/30909
• dc.language.iso eng
• dc.publisher Springerca
• dc.relation.ispartof Theory in Biosciences. 2016;135(1):21-36
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PN/BFU2011-28575
• dc.rights © Springer. http://dx.doi.org/10.1007/s12064-015-0220-8. © The Author(s) 2016. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Co-expression
• dc.subject.keyword Data normalization
• dc.subject.keyword Gene networks
• dc.subject.keyword Spurious correlation
• dc.subject.keyword Log-ratio analysis
• dc.subject.keyword Compositional data
• dc.title How should we measure proportionality on relative gene expression data?ca
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion