runSigPathway: Perform pathway analysis
In sigPathway: Pathway Analysis

Description Usage Arguments Details Value Author(s) References Examples

Performs pathway analysis

runSigPathway(G, minNPS = 20, maxNPS = 500,
              tab, phenotype, nsim = 1000,
              weightType = c("constant", "variable"), ngroups = 2,
              npath = 25, verbose = FALSE, allpathways = FALSE,
              annotpkg = NULL, alwaysUseRandomPerm = FALSE)

`G`	a list containing the source, title, and probe sets associated with each curated pathway
`minNPS`	an integer specifying the minimum number of probe sets in `tab` that should be in a gene set
`maxNPS`	an integer specifying the maximum number of probe sets in `tab` that should be in a gene set
`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
`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
`npath`	an integer indicating the number of top gene sets to consider from each statistic when ranking the top pathways
`verbose`	a boolean to indicate whether to print debugging messages to the R console
`allpathways`	a boolean to indicate whether to include the top npath pathways from each statistic or just consider the top npath pathways (sorted by the sum of ranks of both statistics) when generating the summary table
`annotpkg`	a character vector specifying the name of the BioConductor annotation package to use to fetch accession numbers, Entrez Gene IDs, gene name, and gene symbols
`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

runSigPathway is a wrapper function that

(1) Selects the gene sets to analyze using selectGeneSets

(2) Calculates NTk and NEk statistics using calculate.NTk and calculate.NEK

(3) Ranks the top npath pathways from each statistic using rankPathways

(4) Summarizes the means, standard deviation, and individual statistics of each probe set in each of the above pathways using getPathwayStatistics

A list containing

`gsList`	a list containing three vectors from the output of the `selectGeneSets` function
`list.NTk`	a list from the output of calculate.NTk
`list.NEk`	a list from the output of calculate.NEk
`df.pathways`	a data frame from `rankPathways` which contains the top pathways' indices in `G`, gene set category, pathway title, set size, NTk statistics, NEk statistics, the corresponding q-values, and the ranks.
`list.gPS`	a list from `getPathwayStatistics` containing `nrow(df.pathways)` data frames corresponding to the pathways listed in `df.pathways`. Each data frame contains the name, mean, standard deviation, the test statistic (e.g., t-test), and the corresponding unadjusted p-value. If `ngroups` = 1, the Pearson correlation coefficient is also returned. If a valid `annotpkg` is specified, the probes' accession numbers, Entrez Gene IDs, gene name, and gene symbols are also returned.
`parameters`	a list of parameters (e.g., `nsim`) used in the analysis

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 and run analysis with 1 wrapper function

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

res.muscle <- runSigPathway(G, 20, 500, tab, phenotype, nsim,
                            weightType, ngroups, npath, verbose,
                            allpathways, annotpkg)

## Summarize results
print(res.muscle$df.pathways)

## Get more information about the probe sets' means and other statistics
## for the top pathway in res.pathways
print(res.muscle$list.gPS[[1]])

## Write table of top-ranked pathways and their associated probe sets to
## HTML files
writeSigPathway(res.muscle, tempdir(), "sigPathway_rSP",
                "TopPathwaysTable.html")