deds.pval integrates different $p$ values of differential
expression (DE) to rank and select a set of DE genes.
deds.pval(X, E = rep(0, ncol(X)), adj = c("fdr", "adjp"), B = 200, nsig = nrow(X))adj="fdr", False Discovery Rate is controlled and q values
are returned.
If adj="adjp", adjusted $p$ values that controls family wise
type I error rate is returned.B should be 0 (zero) or any number not less than the total
number of permutations.DEDS. See DEDS-class.
deds.pval summarizes $p$ values from multiple statistical models
for the evidence of DE. The DEDS methodology treats each gene as
a point corresponding to a gene's vector of DE measures. An "extreme
origin" is defined as the point that indicate DE, typically a vector
of zero $p$ values. The distance from all points to the extreme is
computed and the ranking of a gene for DE is determined by the
closeness of the gene to the extreme. To determine a cutoff for
declaration of DE, null referent distributions are generated using an
approach similar to the gap statistic (see Reference below). DEDS can also summarize
different statistics, see deds.stat and
deds.stat.linkC.
Yang, Y.H., Xiao, Y. and Segal M.R.: Selecting differentially expressed genes from microarray experiment by sets of statistics. Bioinformatics 2005 21:1084-1093.
deds.stat, deds.stat.linkC.