\HeaderA{calculate.NGSk}{Calculate NGSk (NTk-like) statistics with gene label permutation}{calculate.NGSk}
\keyword{array}{calculate.NGSk}
\keyword{htest}{calculate.NGSk}
\begin{Description}\relax
Calculates the NGSk (NTk-like) statistics with gene label permutation
and the corresponding p-values and q-values for each selected pathway.
\end{Description}
\begin{Usage}
\begin{verbatim}
calculate.NGSk(statV, gsList, nsim = 1000, verbose = FALSE,
               alwaysUseRandomPerm = FALSE)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{statV}] a numeric vector of test statistic (not p-values) for
each individual probe/gene
\item[\code{gsList}] a list containing three vectors from the output of
the \code{selectGeneSets} function
\item[\code{nsim}] an integer indicating the number of permutations to use
\item[\code{verbose}] a boolean to indicate whether to print debugging messages
to the R console
\item[\code{alwaysUseRandomPerm}] a boolean to indicate whether the algorithm
can use complete permutations for cases where \code{nsim} is greater
than the total number of unique permutations possible with the
\code{phenotype} vector
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
This function is a generalized version of NTk calculations;
\code{calculate.NTk} calls this function internally.  To use this
function, the user must specify a vector of test statistics (e.g.,
t-statistic, Wilcoxon).  Pathways from this function can be ranked
with \code{rankPathways.NGSk} or with \code{rankPathways} when
combined with results from another pathway analysis algorithm (e.g.,
\code{calculate.NEk}).
\end{Details}
\begin{Value}
A list containing
\begin{ldescription}
\item[\code{ngs}] number of gene sets
\item[\code{nsim}] number of permutations performed
\item[\code{t.set}] a numeric vector of Tk/Ek statistics
\item[\code{t.set.new}] a numeric vector of NTk/NEk statistics
\item[\code{p.null}] the proportion of nulls
\item[\code{p.value}] a numeric vector of p-values
\item[\code{q.value}] a numeric vector of q-values
\end{ldescription}
\end{Value}
\begin{Author}\relax
Lu Tian, Peter Park, and Weil Lai
\end{Author}
\begin{References}\relax
Tian L., Greenberg S.A., Kong S.W., Altschuler J., Kohane I.S., Park
P.J. (2005)  Discovering statistically significant pathways in
expression profiling studies.  \emph{Proceedings of the National
Academy of Sciences of the USA}, \bold{102}, 13544-9.

\url{http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102}
\end{References}
\begin{Examples}
\begin{ExampleCode}
## Load in filtered, expression data
data(MuscleExample)

## Prepare the pathways to analyze
probeID <- rownames(tab)
gsList <- selectGeneSets(G, probeID, 20, 500)

nsim <- 1000
ngroups <- 2
verbose <- TRUE
weightType <- "constant"
methodName <- "NGSk"
npath <- 25
allpathways <- FALSE
annotpkg <- "hgu133a"

statV <- calcTStatFast(tab, phenotype, ngroups)$tstat
res.NGSk <- calculate.NGSk(statV, gsList, nsim, verbose)

## Summarize top pathways from NGSk
res.pathways.NGSk <-
  rankPathways.NGSk(res.NGSk, G, gsList, methodName, npath)
print(res.pathways.NGSk)

## Get more information about the probe sets' means and other statistics
## for the top pathway in res.pathways.NGSk
gpsList <-
  getPathwayStatistics.NGSk(statV, probeID, G, res.pathways.NGSk$IndexG,
                            FALSE, annotpkg)
print(gpsList[[1]])

## Write table of top-ranked pathways and their associated probe sets to
## HTML files
parameterList <-
  list(nprobes = nrow(tab), nsamples = ncol(tab),
       phenotype = phenotype, ngroups = ngroups,
       minNPS = 20, maxNPS = 500, ngs = res.NGSk$ngs,
       nsim.NGSk = res.NGSk$nsim,
       annotpkg = annotpkg, npath = npath, allpathways = allpathways)

writeSP(res.pathways.NGSk, gpsList, parameterList, tempdir(),
        "sigPathway_cNGSk", "TopPathwaysTable.html")

\end{ExampleCode}
\end{Examples}

