RT Journal Article SR Electronic T1 Proportionality: a valid alternative to correlation for relative data JF bioRxiv FD Cold Spring Harbor Laboratory SP 008417 DO 10.1101/008417 A1 David Lovell A1 Vera Pawlowsky-Glahn A1 Juan José Egozcue A1 Samuel Marguerat A1 Jürg Bähler YR 2014 UL http://biorxiv.org/content/early/2014/08/25/008417.abstract AB 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 in-appropriate 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.