propr: Calculating Proportionality Between Vectors of Compositional Data

The bioinformatic evaluation of gene co-expression often begins with correlation-based analyses. However, this approach lacks statistical validity when applied to relative count data. This includes, for example, biological data produced by high-throughput RNA-sequencing, chromatin immunoprecipitation (ChIP), ChIP-sequencing, Methyl-Capture sequencing, and other techniques. Two metrics of proportionality, phi [Lovell et al (2015) <doi:10.1371/journal.pcbi.1004075>] and rho [Erb and Notredame (2016) <doi:10.1007/s12064-015-0220-8>], both derived from compositional data analysis, a branch of math dealing specifically with relative data, represent novel alternatives to correlation. This package introduces a programmatic framework for calculating feature dependence through proportionality, as discussed in the cited publications.

Version: 2.1.2
Depends: R (≥ 3.2.2)
Imports: fastcluster, ggplot2, methods, Rcpp, stats, utils
LinkingTo: Rcpp
Suggests: cccrm, compositions, data.table, grid, ggdendro, igraph, knitr, plotly, reshape2, rmarkdown, testthat
Published: 2017-02-02
Author: Thomas Quinn [aut, cre], David Lovell [aut], Anders Bilgrau [ctb], Ionas Erb [ctb]
Maintainer: Thomas Quinn <contacttomquinn at>
License: GPL-2
NeedsCompilation: yes
Citation: propr citation info
Materials: README NEWS
CRAN checks: propr results


Reference manual: propr.pdf
Vignettes: Calculating the Proportionality Coefficients of Compositional Data
Understanding RNA-seq Data through Proportionality Analysis
Package source: propr_2.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Mavericks binaries: r-release: propr_2.1.2.tgz, r-oldrel: propr_2.1.2.tgz
Old sources: propr archive


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