calculate.NGSk {sigPathway} | R Documentation |
Calculates the NGSk (NTk-like) statistics with gene label permutation and the corresponding p-values and q-values for each selected pathway.
calculate.NGSk(statV, gsList, nsim = 1000, verbose = FALSE, alwaysUseRandomPerm = FALSE)
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 |
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
).
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) 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")