crowley-Rpackageforidentifying-2017.pdf (1.65 MB)
propr: An R-package for identifying proportionally abundant features using compositional data analysis
journal contribution
posted on 2017-11-24, 00:00 authored by Thomas Quinn, Mark RichardsonMark Richardson, D Lovell, Tamsyn CrowleyTamsyn CrowleyIn 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.
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Journal
Scientific ReportsVolume
7Issue
1Article number
16252Publisher
Nature Publishing GroupLocation
London, Eng.Publisher DOI
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ISSN
2045-2322eISSN
2045-2322Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2017, The AuthorsUsage metrics
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