Journal
PLOS COMPUTATIONAL BIOLOGY
Volume 11, Issue 3, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1004075
Keywords
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Funding
- Commonwealth Scientific and Industrial Research Organisation
- Spanish Ministry of Education, Culture and Sports under a Salvador de Madariaga grant [PR2011-0290]
- Spanish Ministry of Economy and Competitiveness under the project METRICS [MTM2012-33236]
- Agencia de Gestio d'Ajuts Universitaris i de Recerca of the Generalitat de Catalunya [2009SGR424]
- UK Medical Research Council
- Wellcome Trust Senior Investigator Award [095598/Z/11/Z]
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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.
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