# crit.fun: Find posterior probability threshold to control FDR In EBarrays: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification

## Description

Find posterior probability threshold to control FDR

## Usage

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```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) ```

EBarrays documentation built on Nov. 17, 2017, 12:25 p.m.