Computation of the q-value

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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, holger.schw@gmx.de

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.

See Also

pi0.est,SAM-class,sam

Examples

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## 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)

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