# qvalue.cal: Computation of the q-value In siggenes: Multiple testing using SAM and Efron's empirical Bayes approaches

## Description

Computes the q-values of a given set of p-values.

## Usage

 `1` ``` qvalue.cal(p, p0, version = 1) ```

## Arguments

 `p` a numeric vector containing the p-values. `p0` a numeric value specifying the prior probability that a gene is not differentially expressed. `version` If `version=2`, the original version of the q-value, i.e. min{pFDR}, will be computed. if `version=1`, min{FDR} will be used in the computation of the q-value.

## Details

Using `version = 1` in `qvalue.cal` corresponds to setting `robust = FALSE` in the function `qvalue` of John Storey's R package qvalue, while `version = 2` corresponds to `robust = TRUE`.

## Value

A vector of the same length as `p` containing the q-values corresponding to the p-values in `p`.

## Author(s)

Holger Schwender, [email protected]

## References

Storey, J.D. (2003). The positive False Discovery Rate: A Bayesian Interpretation and the q-value. Annals of Statistics, 31, 2013-2035.

Storey, J.D., and Tibshirani, R. (2003). Statistical Significance for Genome-wide Studies. PNAS, 100, 9440-9445.

`pi0.est`,`SAM-class`,`sam`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```## Not run: # Load the package multtest and the data of Golub et al. (1999) # contained in multtest. library(multtest) data(golub) # Perform a SAM analysis. sam.out<-sam(golub, golub.cl, B=100, rand=123) # Estimate the prior probability that a gene is not significant. pi0 <- pi0.est(sam.out@p.value)\$p0 # Compute the q-values of the genes. q.value <- qvalue.cal(sam.out@p.value, pi0) ## End(Not run) ```

siggenes documentation built on May 2, 2018, 6:07 p.m.