fdrBiCurve: Plots the numbers of null hypothesis rejections

In empiricalFDR.DESeq2: Simulation-Based False Discovery Rate in RNA-Seq

Description

Plots the numbers of null hypothesis rejections in real data and data simulated under null hypothesis (false positives)

Usage

fdrBiCurve(fdr.table, maxLogP = 5, ...)

Arguments

fdr.table	The output of fdrTable(): a dataframe listing p-value cutoffs and the number of null hypothesis rejections at each cutoff in the real and simulated datasets.
maxLogP	Maximal negative decimal logarithm of the p-value for plotting (the default, 5, implies the data for p-values better than 10e-5 will not be plotted)
...	Additional plotting parameters.

Value

The plot is designed to ascertain that the number of discoveries in real data (black line) indeed exceeds the number of false positives (red line) across the range of p-value cutoffs. The grey dotted line is the number of discoveries expected under uniform distribution of p-values.

Author(s)

Mikhail V. Matz

References

R. M. Wright, G. V. Aglyamova, E. Meyer and M. V. Matz (2015) Local and systemic gene expression responses to a white-syndrome-like disease in a reef-bulding coral, Acropora hyacinthus.

Examples

dds = makeExampleDESeqDataSet(betaSD=1, n=100)
dds = DESeq(dds)
sims = simulateCounts(dds)
sims = DESeq(sims)
res = results(dds)
sim.res=results(sims)

# how similar is the simulation to real data? 
plot(sizeFactors(sims)~sizeFactors(dds))
plot(log(dispersions(sims),10)~log(dispersions(dds),10))

# computing and plotting empirical FDR
fdrt = fdrTable(res$pvalue,sim.res$pvalue)
fdrBiCurve(fdrt,maxLogP=4,main="DEG discovery rates")
efdr = empiricalFDR(fdrt,plot=TRUE,main="False discovery rate")

# how many genes pass empirical 0.1 FDR cutoff?
table(res$pvalue<efdr)

# how many genes pass multiplicity-corrected 0.1 FDR cutoff?
table(res$padj<0.1)

empiricalFDR.DESeq2 documentation built on May 2, 2019, 1:10 p.m.