Proportionality: a valid alternative to correlation for relative data

• Published in: PLoS computational biology 2015-03, Vol.11 (3), p.e1004075-e1004075
• Main Authors: Lovell, David, Pawlowsky-Glahn, Vera, Egozcue, Juan José, Marguerat, Samuel, Bähler, Jürg
• Format: Article
• Language: English
• Subjects: Analysis; Associations; Computational Biology - methods; Correlació (Estadística); Correlation (Statistics); Data analysis; Expressió gènica; Gene expression; Gene Expression Regulation, Fungal - genetics; Genetic aspects; Identification and classification; Life sciences; Methods; Models, Genetic; Models, Statistical; Mostreig (Estadística); Mètodes estadístics; Probabilitats; Probabilities; Research Design; RNA, Fungal - genetics; RNA, Messenger - genetics; Sampling (Statistics); Statistical methods; Studies; Yeasts (Fungi); Yeasts - genetics
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004075

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.