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

DL was funded by the Commonwealth Scientific and Industrial Research Organisation (www.csiro.au). VPG was funded by the Spanish Ministry of Education, Culture and Sports under a Salvador de Madariaga grant (Ref. PR2011-0290) and by the Spanish Ministry of Economy and Competitiveness under the project METRICS Ref. MTM2012-33236. JJE was funded by the Agencia de Gestio d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya under project Ref: 2009SGR424. SM was funded by the UK Medical Research Council. JB was funded by a Wellcome Trust Senior Investigator Award (grant #095598/Z/11/Z)