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

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Authors Thomas P. Quinn, Mark F. Richardson, David Lovell, Tamsyn M. Crowley
Journal/Conference Name Scientific Reports
Paper Category
Paper Abstract In the life sciences, many assays measure only the relative abundances of components for each sample. These data, called compositional data, require special handling in order to avoid misleading conclusions. For example, in the case of correlation, treating relative data like absolute data can lead to the discovery of falsely positive associations. 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 two proposed 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.
Date of publication 2017
Code Programming Language R

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