HyperGResult
class from the Category package to perform enrichment
calculation quickly for multiple gene sets.
"summary"(object, pvalue = pvalueCutoff(object), categorySize = NULL)
"htmlReport"(r, file = "", append = FALSE, label = "", digits = 3, summary.args = NULL)
"pvalues"(r)
"sigCategories"(r, p)
"geneCounts"(r)
"expectedCounts"(r)
"oddsRatios"(r)
"universeCounts"(r)
"geneMappedCount"(r)
"universeMappedCount"(r)
"geneIdsByCategory"(r, catids = NULL)
"geneIdUniverse"(r, cond = FALSE)ListHyperGResult object.NULL. If
not NULL, then it gives the minimum number of annotated genes
in the universe, in order to list the term."", then the result is written to the standard
output. If it is NULL, then the result is not written
anywhere. (But it is always returned, invisibly, see below.)summary
method.NULL.pvalues, geneCounts, expectedCounts,
oddsRatios and universeCounts return a list of named
numeric vectors.geneMappedCount returns a numeric vector,
universeMappedCount returns a numeric vector of length one.sigCategories returns a list of character vectors.geneIdsByCategory returns a list of lists of character vectors.geneIdUniverse returns a list of character vectors.summary returns a list of data frames with columns:
‘Pvalue’, ‘OddsRatio’, ‘ExpCount’,
‘Count’, ‘Size’ and optionally ‘drive’.htmlReport returns a list of chracter vectors, invisibly.conditional returns a logical vector of length one.
ontology returns a character vector of length one.
HyperGResult in the Category package. Usually the only
difference is that they return a list of vectors, with one entry for
each gene set, instead of just a single vector. pvalues returns the $p$-values of the hypergeomatric
tests. A list is returned, with one numeric vector entry for each
input gene set. The $p$-values for each gene set are ordered
according to decreasing significance. geneCounts returns the number of genes from the gene set that
are annotated with the given term. This is returned for all input gene
sets, in a list. expectedCounts returns the number of genes that are expected to
be annotated with the given term, just by chance. This is calculated
for all input gene sets, and returned as a list. oddsRatios returns the odds ratios for each term tested, for
all gene sets, in a list of numeric vectors. universeCounts returns the number of genes from the universe
that are annotated with the given term, for all gene sets, in a list. geneMappedCount gives the size of the gene sets, as used in the
algorithm. This can be different than the size of the input gene sets,
because of the elimination of duplicates and genes that are not in the
universe, before the actual computation. universeMappedCount gives the size of the gene universe, as
used in the computation. This can be different than the size given by
the user, because duplicates are eliminated before the computation. sigCategories returns the significant terms, at the given
$p$-value threshold, for all gene sets, as a list. geneIdsByCategory returns a list of lists, one entry for each
input gene set. Every entry is a list itself and for each tested term
it gives the gene ids from the gene set that are annotated with the
given term. geneIdUniverse returns a list of character vectors, one for
each term that was tested, giving the ids of the genes from the
universe that are annotated with that term. summary returns a list of data frames, one for each input gene
set. Each data frame has columns:
‘Pvalue’, ‘OddsRatio’, ‘ExpCount’,
‘Count’, ‘Size’ and optionally ‘drive’.
Each row of the data frame corresponds to a tested term. htmlReport creates a HTML summary from a
ListHyperGParams object. This consists of one table for each
input gene get. The summary can be written to a file, but it is also
returned in a list of character vectors. There is one list entry for
each input gene set, and each element of the character vector
corresponds to one line of HTML code. You need the xtable
package to use this function. The following functions are defined for GOListHyperGResult
objects only. conditional returns a logical vector of length one, whether the
test was conditional or not. Conditional testing is currently not
implemented, please see the GOstats package for a working
implementation. ontology returns a character vector of length one, the name of
the ontology for the GO test.ListHyperGResult object can store the results of
hypergeometric tests, several gene sets against the same
universe. ListHyperGRresult is an extension of
HyperGResult, as defined in the Category package. More precisely, ListHyperGResult is an abstract class, it is
not possible to instantiate objects from it. Its extensions are be
used instead:
GOListHyperGResult, KEGGListHyperGResult,
CHRListHyperGResult and miRNAListHyperGResult.
ISAGO, ISAKEGG, ISACHR,
ISAmiRNA, ISAEnrichment.
Perhaps see also the vignette in the GOstats package.
data(ALLModulesSmall)
GO <- ISAGO(ALLModulesSmall)
GO$CC
sigCategories(GO$CC)[[1]]
summary(GO$CC)[[1]][,1:5]
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