Quick start

Welcome to the propr GitHub page!

The bioinformatic evaluation of gene co-expression often begins with correlation-based analyses. However, this approach lacks statistical validity when applied to relative data. This includes, for example, biological count data generated by high-throughput RNA-sequencing, chromatin immunoprecipitation (ChIP), ChIP-sequencing, Methyl-Capture sequencing, and other techniques. 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)

Further reading

To learn more about proportionality, we refer the reader to the 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. bioRxiv: doi:10.1101/104935
## 
##   Erb I, Quinn T, Lovell D, Notredame C (2017) Differential
##   Proportionality - A Normalization-Free Approach To Differential
##   Gene Expression. bioRxiv: doi:10.1101/134536
## 
##   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