TY - JOUR T1 - Proportionality: a valid alternative to correlation for relative data JF - bioRxiv DO - 10.1101/008417 SP - 008417 AU - David Lovell AU - Vera Pawlowsky-Glahn AU - Juan José Egozcue AU - Samuel Marguerat AU - Jürg Bähler Y1 - 2014/01/01 UR - http://biorxiv.org/content/early/2014/08/25/008417.abstract N2 - 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. ER -