Proportionality : a valid alternative to correlation for relative data

Lovell, David, Pawlowsky-Glahn, Vera, Egozcue, Juan José, Marguerat, Samuel, & Bähler, Jürg (2015) Proportionality : a valid alternative to correlation for relative data. PLoS Computational Biology, 11(3), e1004075.

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Abstract

Relative abundance data is common in the life sciences, but appreciation that it needs special analysis and interpretation is scarce. Correlation is popular as a statistical measure of pairwise association but should not be used on data that carry only relative information. Using timecourse yeast gene expression data, we show how correlation of relative abundances can lead to conclusions opposite to those drawn from absolute abundances, and that its value changes when different components are included in the analysis. Once all absolute information has been removed, only a subset of those associations will reliably endure in the remaining relative data, specifically, associations where pairs of values behave proportionally across observations. We propose a new statistic φ to describe the strength of proportionality between two variables and demonstrate how it can be straightforwardly used instead of correlation as the basis of familiar analyses and visualization methods.

Impact and interest:

13 citations in Scopus
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12 citations in Web of Science®

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45 since deposited on 31 Mar 2015
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ID Code: 82997
Item Type: Journal Article
Refereed: Yes
DOI: 10.1371/journal.pcbi.1004075
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Lovell et al.
Copyright Statement: This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Deposited On: 31 Mar 2015 03:19
Last Modified: 30 Mar 2016 09:16

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