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PLoS Comput Biol. 2015 Mar 16;11(3):e1004075. doi: 10.1371/journal.pcbi.1004075. eCollection 2015 Mar.

Proportionality: a valid alternative to correlation for relative data.

Author information

1
Queensland University of Technology, Brisbane, Australia.
2
Dept. d'Informàtica, Matemàtica Aplicada i Estadística. U. de Girona, España.
3
Dept. Applied Mathematics III, U. Politécnica de Catalunya, Barcelona, Spain.
4
MRC Clinical Sciences Centre, Imperial College London, United Kingdom.
5
Research Department of Genetics, Evolution and Environment, University College London, United Kingdom.

Abstract

In the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative-or compositional-data, differential expression needs careful interpretation, and correlation-a statistical workhorse for analyzing pairwise relationships-is an inappropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statistic ϕ which can be used instead of correlation as the basis of familiar analyses and visualisation methods, including co-expression networks and clustered heatmaps. While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes.

PMID:
25775355
PMCID:
PMC4361748
DOI:
10.1371/journal.pcbi.1004075
[Indexed for MEDLINE]
Free PMC Article
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