'crit_fun' calculates the soft threshold for a target FDR.
The posterior probabilities of being EE.
The target FDR.
Regarding a target FDR alpha, both hard threshold and soft threshold could be used. If the hard threshold is preferred, user could simply take the transcripts with PP(DE) greater than (1-alpha). Using the hard threshold, any DE transcript in the list is with FDR <= alpha.
If the soft threshold is preferred, user could take the transcripts with PP(DE) greater than crit_fun(PPEE, alpha). Using the soft threshold, the list of DE transcripts is with average FDR alpha.
The adjusted FDR threshold of target FDR.
Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013)
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data(GeneMat) GeneMat.small = GeneMat[c(1:10, 500:600),] Sizes = MedianNorm(GeneMat.small) EBOut = EBTest(Data = GeneMat.small, Conditions = as.factor(rep(c("C1","C2"), each=5)), sizeFactors = Sizes, maxround = 5) PP = GetPPMat(EBOut) DEfound = rownames(PP)[which(PP[,"PPDE"] >= 0.95)] str(DEfound) SoftThre = crit_fun(PP[,"PPEE"], 0.05) DEfound_soft = rownames(PP)[which(PP[,"PPDE"] >= SoftThre)]
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