gCMAP (version 1.16.0)

gsealm_jg_score-methods: Parametric test for testing normally distributed scores for gene set enrichment

Description

This method implements the 'JG' summary method introduced by Oron et al, 2008, as a stand-alone method to query a set of normally-distributed scores (e.g. t-statistics or z-scores) with gene sets (and vice versa).

Scores for gene-set members are summed, respecting their sign (up- or down-regulated) and the combined score is divided by the square-root of the number of set members. To fit linear models to an expression profiling experiment instead, please use the gsealm_jg_score method instead.

Arguments

query

An eSet, CMAPCollection, GeneSetCollection,GeneSet, matrix or numeric vector with data and gene ids. If a matrix is provided, gene ids must be provided as row-names. If a vector is provided, gene ids must be provided as names.

sets

See 'query'

removeShift

Optional parameter indicating that the aggregated test statistic should be centered by subtracting the column means (default=FALSE).Note: this option is not available for analysis of big.matrix backed eSet objects.

element

For eSet objects, which assayDataElement should be extracted ? (Default="exprs")

keep.scores

Logical: keep gene-level scores for all gene sets (Default: FALSE) ? The size of the generated CMAPResults object increases with the number of contained gene sets. For very large collections, setting this parameter to 'TRUE' may require large amounts of memory.

...

Additional arguments to be passed on to downstream methods.

Value

A CMAPResults object or, in case of multi-dimensional queries, a list of CMAPResults objects.

Methods

signature(query = "CMAPCollection", sets = "eSet")

signature(query = "CMAPCollection", sets = "matrix")

signature(query = "CMAPCollection", sets = "numeric")

signature(query = "eSet", sets = "CMAPCollection")

signature(query = "eSet", sets = "GeneSet")

signature(query = "eSet", sets = "GeneSetCollection")

signature(query = "GeneSet", sets = "eSet")

signature(query = "GeneSet", sets = "matrix")

signature(query = "GeneSet", sets = "numeric")

signature(query = "GeneSetCollection", sets = "eSet")

signature(query = "GeneSetCollection", sets = "matrix")

signature(query = "GeneSetCollection", sets = "numeric")

signature(query = "matrix_or_big.matrix", sets = "CMAPCollection")

signature(query = "matrix", sets = "GeneSet")

signature(query = "matrix", sets = "GeneSetCollection")

signature(query = "numeric", sets = "CMAPCollection")

signature(query = "numeric", sets = "GeneSet")

signature(query = "numeric", sets = "GeneSetCollection")

References

Gene set enrichment analysis using linear models and diagnostics. Oron AP, Jiang Z, Gentleman R. Bioinformatics. 2008 Nov 15;24(22):2586-91. Epub 2008 Sep 11.

Extensions to gene set enrichment. Jiang Z, Gentleman R. Bioinformatics. 2007 Feb 1;23(3):306-13. Epub 2006 Nov 24.

Examples

data(gCMAPData)
gene.set.collection <- induceCMAPCollection(gCMAPData, "z", higher=2, lower=-2)

## comparison with a single user-provided profile of z-scores
profile <- assayDataElement(gCMAPData, "z")[,1]
gsealm_jg_score(profile, gene.set.collection)

## comparison with of multiple profiles of z-scores to the CMAPCollection
res <- gsealm_jg_score(assayDataElement(gCMAPData, "z"), gene.set.collection)

## first CMAPResult object
res[[1]]

## adjusted p-values from all CMAPResult objects
sapply(res, padj)

## inverted query: CMAPCollection is compared to z-score profiles
gsealm_jg_score(gene.set.collection, assayDataElement(gCMAPData, "z"))[[1]]