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 data. This includes, for example, biological count data generated by high-throughput RNA-sequencing, chromatin immunoprecipitation (ChIP), ChIP-sequencing, Methyl-Capture sequencing, and other techniques. This package implements two metrics, phi [Lovell et al (2015) <doi:10.1371/journal.pcbi.1004075>] and rho [Erb and Notredame (2016) <doi:10.1007/s12064-015-0220-8>], to provide a valid alternatives to correlation for relative data. Unlike correlation, these metrics give the same result for both relative and absolute data. Pairs that are strongly proportional in relative space are also strongly correlated in absolute space. Proportionality avoids the pitfall of spurious correlation.

Version: 2.2.0
Depends: methods, R (≥ 3.2.2)
Imports: fastcluster, ggplot2, igraph, Rcpp, stats, utils
LinkingTo: Rcpp
Suggests: ALDEx2, cccrm, compositions, data.table, grid, ggdendro, knitr, plotly, reshape2, rgl, rmarkdown, testthat
Published: 2017-04-19
Author: Thomas Quinn [aut, cre], David Lovell [aut], Ionas Erb [ctb], Anders Bilgrau [ctb], Greg Gloor [ctb]
Maintainer: Thomas Quinn <contacttomquinn at gmail.com>
BugReports: http://github.com/tpq/propr/issues
License: GPL-2
URL: http://github.com/tpq/propr
NeedsCompilation: yes
Citation: propr citation info
Materials: README NEWS
CRAN checks: propr results

Downloads:

Reference manual: propr.pdf
Vignettes: 1. Calculating the Proportionality Coefficients of Compositional Data
Frequently Asked Questions
2. Understanding RNA-seq Data through Proportionality Analysis
Package source: propr_2.2.0.tar.gz
Windows binaries: r-devel: propr_2.2.0.zip, r-release: propr_2.2.0.zip, r-oldrel: propr_2.2.0.zip
OS X El Capitan binaries: r-release: propr_2.2.0.tgz
OS X Mavericks binaries: r-oldrel: propr_2.1.8.tgz
Old sources: propr archive

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