empiricalFDR: Computing the p-value cutoff to achieve a given FDR.

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

Description

This function calculates the cutoff at which a particular false discovery rate is observed using loess smoothing and interpolation.

Usage

empiricalFDR(fdr.table, FDR = 0.1, maxLogP = 5, plot = FALSE, span = 0.1, ...)

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.
FDR	The target false discovery rate.
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)
plot	Whether to produce a plot.
span	span parameter for loess smoothing.
...	Additional plotting parameters.

Value

The function returns a single value, which is the p-value cutoff at which the target FDR is observed. With plot = TRUE, also plots the observed experimental FDRs with the loess smoother.

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.