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 count data. This includes, for example, those biological data produced by microarray assays or high-throughput RNA-sequencing. This package provides a set of functions for measuring dependence between relative features using compositional data analysis. Specifically, this package implements two measures of proportionality, φ and ρ, introduced in Lovell 2015 and expounded in Erb 2016. You can get started with propr by installing the most up-to-date version of this package directly from GitHub.

library(devtools)
devtools::install_github("tpq/propr")
library(propr)

The principal functions in propr include: (1) phit, for the calculation of φ, and (2) perb, for the calculation of ρ. In the example below, we calculate these proportionality metrics for a simulated dataset, printing the results as a proportionality matrix and then as a pairwise summary. We refer you to the official package vignette for a comprehensive discussion of compositional data, proportionality, and everything this package has to offer.

set.seed(12345)
N <- 10
data.absolute <- data.frame(a=(1:N), b=(1:N) * rnorm(N, 10, 0.1),
                            c=(N:1), d=(N:1) * rnorm(N, 10, 1.0))
data.relative <- data.absolute / colSums(data.absolute)

Calculate φ

phi <- phit(data.relative)

## Calculating all phi for actual counts...

phi@matrix

##             a           b          c          d
## a 0.000000000 0.001894476 3.95056338 4.02312199
## b 0.001894476 0.000000000 3.97849497 4.05353543
## c 3.950563382 3.978494970 0.00000000 0.01119647
## d 4.023121991 4.053535432 0.01119647 0.00000000

phi@pairs

##   feature1 feature2        prop
## 1        a        b 0.001894476
## 2        c        d 0.011196465
## 3        a        c 3.950563382
## 4        b        c 3.978494970
## 5        a        d 4.023121991
## 6        b        d 4.053535432

Calculate ρ

rho <- perb(data.relative)

## Calculating all rho for actual counts...

rho@matrix

##            a          b          c          d
## a  1.0000000  0.9990459 -0.9985539 -0.9982335
## b  0.9990459  1.0000000 -0.9981875 -0.9985699
## c -0.9985539 -0.9981875  1.0000000  0.9945048
## d -0.9982335 -0.9985699  0.9945048  1.0000000

rho@pairs

##   feature1 feature2       prop
## 1        a        b  0.9990459
## 2        b        d -0.9985699
## 3        a        c -0.9985539
## 4        a        d -0.9982335
## 5        b        c -0.9981875
## 6        c        d  0.9945048

References

1. Erb, I. & Notredame, C. 2016. How should we measure proportionality on relative gene expression data? Theory Biosci.

2. Lovell, D. et al. 2015. Proportionality: A Valid Alternative to Correlation for Relative Data. PLoS Comput Biol 11.