sigPathway (version 1.40.0)

calculate.NGSk: Calculate NGSk (NTk-like) statistics with gene label permutation

Description

Calculates the NGSk (NTk-like) statistics with gene label permutation and the corresponding p-values and q-values for each selected pathway.

Usage

calculate.NGSk(statV, gsList, nsim = 1000, verbose = FALSE, alwaysUseRandomPerm = FALSE)

Arguments

statV

a numeric vector of test statistic (not p-values) for each individual probe/gene

gsList

a list containing three vectors from the output of the selectGeneSets function

nsim

an integer indicating the number of permutations to use

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

Value

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

Details

This function is a generalized version of NTk calculations; 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 rankPathways.NGSk or with rankPathways when combined with results from another pathway analysis algorithm (e.g., calculate.NEk).

References

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

Examples

## 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.db"

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")