calculatePathwayStatistics {sigPathway} | R Documentation |
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")