propr: An R-package for Identifying Proportionally Abundant Features Using Compositional Data Analysis

• Published in: Scientific reports Vol. 7; no. 1; p. 16252
• Main Authors: Quinn, Thomas P., Richardson, Mark F., Lovell, David, Crowley, Tamsyn M.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24-11-2017; Nature Publishing Group
• Subjects: 45/91; 631/114/2163; 631/114/794; Algorithms; Animals; Data Analysis; Data Interpretation, Statistical; Data processing; Gene expression; Genomics - methods; Humanities and Social Sciences; Humans; Libraries; multidisciplinary; Relative abundance; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-017-16520-0

In the life sciences, many assays measure only the relative abundances of components in each sample. Such data, called
compositional
data, require special treatment to avoid misleading conclusions. Awareness of the need for caution in analyzing compositional data is growing, including the understanding that correlation is not appropriate for relative data. Recently, researchers have proposed proportionality as a valid alternative to correlation for calculating pairwise association in relative data. Although the question of how to best measure proportionality remains open, we present here a computationally efficient R package that implements three measures of proportionality. In an effort to advance the understanding and application of proportionality analysis, we review the mathematics behind proportionality, demonstrate its application to genomic data, and discuss some ongoing challenges in the analysis of relative abundance data.