Welcome to the propr GitHub page!
The bioinformatic evaluation of gene co-expression often begins with correlation-based analyses. However, correlation lacks validity when applied to relative data, including count data generated by next-generation sequencing. This package implements several metrics for proportionality, including phi [Lovell et al (2015)] and rho [Erb and Notredame (2016)]. This package also implements several metrics for differential proportionality. Unlike correlation, these measures give the same result for both relative and absolute data.
library(devtools)
devtools::install_github("tpq/propr")
library(propr)
?proprTo learn more about proportionality, please see the vignettes and relevant literature.
citation("propr")##
## To cite propr in publications use:
##
## Quinn T, Richardson MF, Lovell D, Crowley T (2017) propr: An
## R-package for Identifying Proportionally Abundant Features Using
## Compositional Data Analysis. Scientific Reports 7(16252):
## doi:10.1038/s41598-017-16520-0
##
## Erb I, Quinn T, Lovell D, Notredame C (2017) Differential
## Proportionality - A Normalization-Free Approach To Differential
## Gene Expression. Proceedings of CoDaWork 2017, The 7th
## Compositional Data Analysis Workshop; available under bioRxiv
## 134536: doi:10.1101/134536
##
## Quinn T, Erb I, Richardson MF, Crowley T (2018) Understanding
## sequencing data as compositions: an outlook and review.
## Bioinformatics 34(16): doi:10.1093/bioinformatics/bty175
##
## Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, Bahler J
## (2015) Proportionality: A Valid Alternative to Correlation for
## Relative Data. PLoS Comput Biol 11(3):
## doi:10.1371/journal.pcbi.1004075
##
## Erb I, Notredame C (2016) How should we measure proportionality
## on relative gene expression data? Theory Biosci 135(1):
## doi:10.1007/s12064-015-0220-8
##
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.
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