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) ] and rho [Erb and Notredame (2016) ], 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.

Maintainer: Thomas Quinn <contacttomquinn at gmail.com>

Author(s): Thomas Quinn*, David Lovell*, Ionas Erb*, Anders Bilgrau*, Greg Gloor*

Install package and any missing dependencies by running this line in your R console:

install.packages("propr")

Depends R (>= 3.2.2)
Imports fastcluster, ggplot2, igraph, methods, Rcpp, stats, utils
Suggests ALDEx2, cccrm, compositions, data.table, grid, ggdendro, knitr, plotly, reshape2, rgl, rmarkdown, testthat
Enhances
Linking to Rcpp
Reverse
depends
Reverse
imports
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suggests
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enhances
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linking to

Package propr
Materials
URL http://github.com/tpq/propr
Task Views
Version 2.1.8
Published 2017-03-09
License GPL-2
BugReports http://github.com/tpq/propr/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks propr check results
Package source propr_2.1.8.tar.gz