compositions, dendextend, fastcluster, ggplot2, ggthemes,
knitr, rmarkdown, testthat

c(
person("Thomas", "Quinn", email = "contacttomquinn@gmail.com", role = c("aut", "cre")),
person("David", "Lovell", email = "david.lovell@qut.edu.au", role = "aut"),
person("Anders", "Bilgrau", email = "anders.ellern.bilgrau@gmail.com", role = "ctb"),
person("Ionas", "Erb", email = "ionas.erb@crg.eu", role = "ctb")
)
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.
fastcluster, ggplot2, methods, Rcpp, stats, utils
cccrm, compositions, data.table, grid, ggdendro, igraph,
knitr, plotly, reshape2, rmarkdown, testthat
Thomas Quinn [aut, cre],
David Lovell [aut],
Anders Bilgrau [ctb],
Ionas Erb [ctb]

c(
person("Thomas", "Quinn", email = "contacttomquinn@gmail.com", role = c("aut", "cre")),
person("David", "Lovell", email = "david.lovell@qut.edu.au", role = "aut"),
person("Ionas", "Erb", email = "ionas.erb@crg.eu", role = "ctb"),
person("Anders", "Bilgrau", email = "anders.ellern.bilgrau@gmail.com", role = "ctb"),
person("Greg", "Gloor", email = "ggloor@uwo.ca", role = "ctb")
)
fastcluster, ggplot2, igraph, methods, Rcpp, stats, utils
ALDEx2, cccrm, compositions, data.table, grid, ggdendro,
knitr, plotly, reshape2, rgl, rmarkdown, testthat
Thomas Quinn [aut, cre],
David Lovell [aut],
Ionas Erb [ctb],
Anders Bilgrau [ctb],
Greg Gloor [ctb]