\title{CoGAPS gene set statistic}
 Computes the p-value for the association of underlying patterns from microarray data to activity in gene sets.}
   calcCoGAPSStat(Amean, Asd, GStoGenes, numPerm=500)}
 \item{Amean}{Sampled mean value of the amplitude matrix \eqn{{\bf{A}}}.  \code{row.names(Amean)} must correspond to the gene names contained in GStoGenes.}
 \item{Asd}{Sampled standard deviation of the amplitude matrix \eqn{{\bf{A}}}.}
 \item{GStoGenes}{List or data frame containing the genes in each gene set. If a list, gene set names are the list names and corresponding elements are the names of genes contained in each set. If a data frame, gene set names are in the first column and corresponding gene names are listed in rows beneath each gene set name.}
 \item{numPerm}{Number of permuations used for the null distribution in the gene set statistic. (optional; default=500)}
   This script links the patterns identified in the columns of \eqn{\bf{P}}  to activity in each of the gene sets specified in GStoGenes using a novel z-score based statistic developed in Ochs et al. (2009).  Specifically, the z-score for pattern \eqn{p} and gene set \eqn{G_{i}} containing $G$ total genes is given by \deqn{Z_{i,p} = \frac{1}{G} \sum_{g in \mathcal{G_{i}}} {\frac{{\bf{A}_{gp}}}{\sigma_{gp}}},} where \eqn{g} indexes the genes in the set and \eqn{\sigma_{gp}} is the standard deviation of \eqn{{\bf{A}}_{gp}} obtained from MCMC sampling.  CoGAPS then uses the specified \code{numPerm} random sample tests to compute a consistent p value estimate from that z score.
   A list containing:
   \item{GSUpreg}{p-values for upregulation of each gene set in each pattern.}
   \item{GSDownreg}{p-values for downregulation of each gene set in each pattern.}
   \item{GSActEst}{p-values for activity of each gene set in each pattern.}
 \author{Elana J. Fertig \email{}}
 M.F. Ochs, L. Rink, C. Tarn, S. Mburu, T. Taguchi, B. Eisenberg, and A.K. Godwin.  (2009) Detection and treatment-induced changes in signaling pathways in gastrointestinal stromal tumors using transcriptomic data.  Cancer Research, 69:9125-9132.
 \seealso{\code{\link{CoGAPS}}, \code{\link{GAPS}}}