This function subsets the data based on lists of genes, computes a summary statistic for each gene list, and returns the results in a convenient form.
PGSEA(exprs, cl, range = c(25, 500), ref = NULL, center = TRUE, p.value = 0.005, weighted = TRUE, enforceRange=TRUE, ...)
readGmt
can be used to produce a 'smc'
object list from a simple tab-delimited text file. The gene
expression values from each of these gene lists is extracted and a
summary statistic is computed for each subset (or region in the case of chromosomal bands/arms). The expression data must have the same identifiers as the list of
genes being tested. If they are not, the expression data can be
converted using the aggregateExprs
function, that can use a
current annotation environment to convert and condense the gene
expression data.
By default the method set out by Kim and Volsky http://www.biomedcentral.com/1471-2105/6/144 is applied to the gene set.
If weighted==FALSE than the default t.test
function is used.
The function is set up to perform the analysis on individual
samples. For convenient method to analyze groups of samples, see the
"Limma User's Guide" for more information on how to see up a contrast
matrix and perform a linear model fit. The coefficients of the fit
can then be used a input into the PGSEA
function.
This package has not been extensively tested beyond a set of well defined curated pathways using the
Affymetrix platform and significance values represent approximations. Any results should be confirmed
by additional gene set testing methodologies.
datadir <- system.file("extdata", package = "PGSEA")
sample <- readGmt(file.path(datadir, "sample.gmt"))
data(nbEset)
pg <- PGSEA(nbEset,cl=sample,ref=1:5)
print(pg[,-c(1:5)])
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