calculatePathwayStatistics: Calculate the NTk and NEk statistics
In sigPathway: Pathway Analysis

Description Usage Arguments Details Value Author(s) References Examples

Calculates the NTk and NEk statistics and the corresponding p-values and q-values for each selected pathway.

calculate.NTk(tab, phenotype, gsList, nsim = 1000,
              ngroups = 2, verbose = FALSE, alwaysUseRandomPerm = FALSE)
calculate.NEk(tab, phenotype, gsList, nsim = 1000,
              weightType = c("constant", "variable"),
              ngroups = 2, verbose = FALSE, alwaysUseRandomPerm = FALSE)

`tab`	a numeric matrix of expression values, with the rows and columns representing probe sets and sample arrays, respectively
`phenotype`	a numeric (or character if `ngroups` >= 2) vector indicating the phenotype
`gsList`	a list containing three vectors from the output of the `selectGeneSets` function
`nsim`	an integer indicating the number of permutations to use
`weightType`	a character string specifying the type of weight to use when calculating NEk statistics
`ngroups`	an integer indicating the number of groups in the matrix
`verbose`	a boolean to indicate whether to print debugging messages to the R console
`alwaysUseRandomPerm`	a boolean to indicate whether the algorithm can use complete permutations for cases where `nsim` is greater than the total number of unique permutations possible with the `phenotype` vector

These functions calculate the NTk and NEk statistics and the corresponding p-values and q-values for each selected pathway. The output of both functions should be together to rank top pathways with the rankPathways function.

A list containing

`ngs`	number of gene sets
`nsim`	number of permutations performed
`t.set`	a numeric vector of Tk/Ek statistics
`t.set.new`	a numeric vector of NTk/NEk statistics
`p.null`	the proportion of nulls
`p.value`	a numeric vector of p-values
`q.value`	a numeric vector of q-values

Lu Tian, Peter Park, and Weil Lai

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. Proceedings of the National Academy of Sciences of the USA, 102, 13544-9.

http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102

## Load in filtered, expression data
data(MuscleExample)

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

## Calculate NTk and weighted NEk for each gene set
## * Use a higher nsim (e.g., 2500) value for more reproducible results
nsim <- 1000
ngroups <- 2
verbose <- TRUE
weightType <- "constant"
methodNames <- c("NTk", "NEk")
npath <- 25
allpathways <- FALSE
annotpkg <- "hgu133a.db"

res.NTk <- calculate.NTk(tab, phenotype, gsList, nsim, ngroups, verbose)
res.NEk <- calculate.NEk(tab, phenotype, gsList, nsim, weightType,
                         ngroups, verbose)

## Summarize results
res.pathways <- rankPathways(res.NTk, res.NEk, G, tab, phenotype,
                             gsList, ngroups, methodNames, npath, allpathways)
print(res.pathways)

## Get more information about the probe sets' means and other statistics
## for the top pathway in res.pathways
statList <- calcTStatFast(tab, phenotype, ngroups)
gpsList <-
  getPathwayStatistics(tab, phenotype, G, res.pathways$IndexG,
                       ngroups, statList, 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.NTk$ngs,
       nsim.NTk = res.NTk$nsim, nsim.NEk = res.NEk$nsim,
       weightType = weightType,
       annotpkg = annotpkg, npath = npath, allpathways = allpathways)

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