hyperg: Hypergeometric (gene set enrichment) tests on character...
In Category: Category Analysis

Description Usage Arguments Value Author(s) See Also Examples

This function performs a hypergeometric test for over- or under-representation of significant ‘genes’ amongst those assayed in a universe of genes. It provides an interface based on character vectors of identifying member of gene sets and the gene universe.

1 2	hyperg(assayed, significant, universe, representation = c("over", "under"), ...)

`assayed`	A vector of assayed genes (or other identifiers). `assayed` may be a character vector (defining a single gene set) or list of character vectors (defining a collection of gene sets).
`significant`	A vector of assayed genes that were differentially expressed. If `assayed` is a character vector, then `significant` must also be a character vector; likewise when `assayed` is a `list`.
`universe`	A character vector defining the universe of genes.
`representation`	Either “over” or “under”, to indicate testing for over- or under-representation, respectively, of differentially expressed genes.
`...`	Additional arguments, unused.

When invoked with a character vector of assayed genes, a named numeric vector providing the input values, P-value, odds ratio, and expected number of significantly expressed genes.

When invoked with a list of character vectors of assayed genes, a data frame with columns of input values, P-value, odds ratio, and expected number of significantly expressed genes.

Martin Morgan mtmorgan@fhcrc.org with contributions from Paul Shannon.

hyperGTest for convenience functions using Bioconductor annotation resources such as GO.db.

set.seed(123)

## artifical sets -- affy probes grouped by protein family
library(hgu95av2.db)
map <- select(hgu95av2.db, keys(hgu95av2.db), "PFAM")
sets <- Filter(function(x) length(x) >= 10, split(map$PROBEID, map$PFAM))

universe <- unlist(sets, use.names=FALSE)
siggenes <- sample(universe, length(universe) / 20)  ## simulate
sigsets <- Map(function(x, y) x[x %in% y], sets, MoreArgs=list(y=siggenes))

result <- hyperg(sets, sigsets, universe)
head(result)

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'select()' returned 1:many mapping between keys and columns
        assayed significant universe representation            p     odds
PF00001     211           9    11806           over   0.01357584 2.512915
PF00002      42           5    11806           over 3.439682e-07 42.83053
PF00003      19           1    11806           over   0.03016146 36.32407
PF00004      41           2    11806           over  0.008857134  15.4188
PF00005      61           2    11806           over   0.03954921 6.714163
PF00007      13           1    11806           over   0.01422775  81.8125
          expected
PF00001   3.771049
PF00002  0.1494156
PF00003 0.03057767
PF00004  0.1423852
PF00005  0.3151787
PF00007 0.01431476