Proportionality: a valid alternative to correlation for relative data

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...

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Bibliographic Details
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
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1004075