sigPathway (version 1.40.0)

calculatePathwayStatistics: Calculate the NTk and NEk statistics

Description

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

Usage

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)

Arguments

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

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

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.

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)

## 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")