crit.fun {EBarrays} | R Documentation

## Find posterior probability threshold to control FDR

### Description

Find posterior probability threshold to control FDR

### Usage

    crit.fun(x, cc)

### Arguments

`x` | x is one minus the posterior probabilities of being in a specific DE pattern. If there is only one DE pattern, then x is the posterior probabilities of being EE.
`cc` | cc is FDR to be controlled. For example, to control FDR at 0.05, set cc=0.05.

### Value

`crit.fun` returns a threshold so that if used in identifying genes in a specific DE pattern, FDR can be controlled at cc. Those genes with posterior probability of being in that specific DE pattern greater than this threshold are claimed to be in that specific DE pattern.

### Author(s)

Ming Yuan, Ping Wang, Deepayan sarkar, Michael Newton, and Christina Kendziorski

### References

Newton, M.A., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture model. Biostatistics 5, 155-176.

### Examples

    data(gould)
    pattern <- ebPatterns(c("1,1,1,0,0,0,0,0,0,0",
                            "1,2,2,0,0,0,0,0,0,0"))
    gg.em.out <- emfit(gould, family = "GG", hypotheses = pattern, num.iter = 10)
    gg.post.out <- postprob(gg.em.out, gould)$pattern
    gg.crit <- crit.fun(gg.post.out[,1],0.05)
    # number of DE genes
    sum(gg.post.out[,2] > gg.crit)

    pattern4 <- ebPatterns(c("1, 1, 1, 1, 1, 1, 1, 1, 1, 1",
                             "1, 2, 2, 2, 2, 2, 2, 2, 2, 2",
                             "1,2,2,1,1,1,1,1,2,2",
                             "1,1,1,1,1,1,1,1,2,2"))
    gg4.em.out <- emfit(gould, family = "GG", pattern4, num.iter = 10)
    gg4.post.out <- postprob(gg4.em.out, gould)$pattern
    gg4.crit <- crit.fun(1-gg4.post.out[,2], 0.05)
    # number of genes in pattern 2, a DE pattern
    sum(gg4.post.out[,2] > gg4.crit)

[Package EBarrays version 2.56.0 Index]