Prog-Plot

Introduction

Prog-Plot (progressive proportions plot) is a visual tool to help identify the functional relationship between the covariate and the null proportion in RNA-Seq experimments where a multiple tests correction is performed including a covariate. PP-Plot was made to identify this relationship in the context of the correction proposed by Boca and Leek, that's why it presents curves based on different choices of lambda in a progressive manner.

Implementation

1. Download the progplot.R file to your system
2. Install and load gtools and ggplot, the latter is optional if you want to do your own plot
3. In your R code, run the following command (replacing path_to_file with the actual path in your system where the .R file is located)

ppplot <- dget('/path_to_file/progplot.R')

Use

progplot(
  pval,
  cov,
  lambda=seq(0.85,0.9,0.05),
  cuts=round(log2(length(pval))+1),
  plot=True
)

Arguments

• pval: numeric vector containing the p-values corresponding to the tests for differential expression
• cov: numeric vector containing the value of the covariate for each test (must be same lenght as pval)
• lambda: numeric vector, tresholds used to define the response of the model, must be in [0,1)
• cuts: if integer, the number of groups in which the covariate will be divided; if numeric vector of lenght >1, the values of the covariate that define the bins to estimate the null proportion
• plot: logical, if TRUE, a plot will be displayed; if FALSE, the values used to create the plot will be returned and no plot will be displayed

Example

library(gtools)
library(ggplot2)
ppplot=dget('progplot.R')

set.seed(50)
X=seq(0,1, length=1000)
n.pr=exp(X*2)/exp(2)
n.I=rbinom(1000,prob=n.pr,size=1) > 0
p.val=rep(NA,1000) 
p.val[n.I]=runif(sum(n.I))
p.val[!n.I]=rbeta(sum(!n.I),1,3)

progplot(p.val,X, lambda = seq(0.7,0.9,0.05), cuts = 15)