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Publication Detail
Proportionality: a valid alternative to correlation for relative data
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Preprint
  • Authors:
    Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, Bähler J
  • Publication date:
    25/08/2014
  • Status:
    Published
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 visualization 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.
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