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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  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.

Proportionality: A Valid Alternative to Correlation for Relative Data