Details

Autor(en) / Beteiligte

• Lovell, David
• Pawlowsky-Glahn, Vera
• Egozcue, Juan José
• Marguerat, Samuel
• Bähler, Jürg
• Dunbrack Jr, Roland L.

Titel

Proportionality: a valid alternative to correlation for relative data

Ist Teil von

• PLoS computational biology, 2015-03, Vol.11 (3), p.e1004075-e1004075

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2015

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.