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propr: An R-package for identifying proportionally abundant features using compositional data analysis

Quinn, Thomas P., Richardson, Mark, Lovell, David and Crowley, Tamsyn M. 2017, propr: An R-package for identifying proportionally abundant features using compositional data analysis, Scientific Reports, vol. 7, no. 1, doi: 10.1038/s41598-017-16520-0.

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Title propr: An R-package for identifying proportionally abundant features using compositional data analysis
Author(s) Quinn, Thomas P.
Richardson, MarkORCID iD for Richardson, Mark orcid.org/0000-0002-1650-0064
Lovell, David
Crowley, Tamsyn M.ORCID iD for Crowley, Tamsyn M. orcid.org/0000-0002-3698-8917
Journal name Scientific Reports
Volume number 7
Issue number 1
Article ID 16252
Total pages 9
Publisher Nature Publishing Group
Place of publication London, Eng.
Publication date 2017-11-24
ISSN 2045-2322
2045-2322
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
RNA-SEQ
Summary 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.
Language eng
DOI 10.1038/s41598-017-16520-0
Field of Research 110399 Clinical Sciences not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30105311

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.