Calculating Proportionality Between Vectors of Compositional
Data
c(
person("Thomas", "Quinn", email = "contacttomquinn@gmail.com", role = c("aut", "cre")),
person("David", "Lovell", email = "david.lovell@qut.edu.au", role = "aut")
)
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. As an alternative, Lovell
et al propose a proportionality metric, phi, derived from compositional data
analysis, a branch of math dealing specifically with relative data. In a
subsequent publication, Erb and Nicodemus expounded these efforts by elaborating
on another proportionality metric, rho. This package introduces a programmatic
framework for the calculation of feature dependence using proportionality and
other compositional data methods discussed in the cited publications.
compositions, dendextend, ggplot2, ggthemes, knitr,
rmarkdown, testthat
Thomas Quinn [aut, cre],
David Lovell [aut]

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. As an alternative, Lovell
et al propose a proportionality metric, phi, derived from compositional data
analysis, a branch of math dealing specifically with relative data. In a
subsequent publication, Erb and Nicodemus expounded these efforts by elaborating
on another proportionality metric, rho. This package introduces a programmatic
framework for the calculation of feature dependence through proportionality, as
discussed in the cited publications.

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.