The bioinformatic evaluation of gene co-expression often begins with correlation-based analyses. However, this approach lacks statistical validity when applied to relative data, including those biological count data produced by microarray assays or high-throughput RNA-sequencing. 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 the calculation of feature dependence through proportionality, as discussed in the cited publications.
| Version: | 2.0.1 |
| Depends: | R (≥ 3.2.2) |
| Imports: | methods, Rcpp |
| LinkingTo: | Rcpp |
| Suggests: | compositions, dendextend, ggplot2, ggthemes, knitr, rmarkdown, testthat |
| Published: | 2016-09-30 |
| Author: | Thomas Quinn [aut, cre], David Lovell [aut] |
| 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 |
| Materials: | README NEWS |
| CRAN checks: | propr results |
| Reference manual: | propr.pdf |
| Vignettes: |
Calculating the Proportionality Coefficients of Compositional Data |
| Package source: | propr_2.0.1.tar.gz |
| Windows binaries: | r-devel: propr_2.0.1.zip, r-release: propr_2.0.1.zip, r-oldrel: propr_2.0.1.zip |
| OS X Mavericks binaries: | r-release: propr_2.0.1.tgz, r-oldrel: propr_2.0.1.tgz |
| Old sources: | propr archive |
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