qvalue.cal {siggenes} | R Documentation |
Computes the q-values of a given set of p-values.
qvalue.cal(p, p0, version = 1)
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. |
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
.
A vector of the same length as p
containing the q-values
corresponding to the p-values in p
.
Holger Schwender, holger.schw@gmx.de
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.
## 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)