Proportionality: a valid alternative to correlation for relative data
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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. 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.
CitationLovell, David, Pawlowsky-Glahn, V., Egozcue, J. J., Marguerat, S., Baehler, J. Proportionality: a valid alternative to correlation for relative data. "PLOS computational biology", 01 Març 2015, vol. 11, núm. 3, p. 1-12.