predefinedAnalyses {GiANT} | R Documentation |
Predefined enrichment analyses
Description
Predefined analysis configurations that can be used in geneSetAnalysis
Usage
analysis.gsea()
analysis.overrepresentation()
analysis.customOverrepresentation()
analysis.averageCorrelation()
analysis.averageTStatistic()
analysis.globalTest()
analysis.globalAncova()
Details
The above functions return configurations for state-of-the-art analysis pipelines that can be used in geneSetAnalysis
. All configurations are preconfigured collections of standard methods for the different pipeline steps. The following lists the methods chosen for the different steps and their parameters. For more detailed descriptions of these methods, please refer to the linked manual pages.
-
analysis.gsea
defines the Gene Set Enrichment Analysis (GSEA) method by Subramanain et al. Here, the gene-level statistic the absolute correlation calculated bygls.cor
with the associated parameterslabs, method
and a preprocessing bytransformation.abs
. As a gene set statistic, the enrichment score (functiongss.enrichmentScore
with parameterp
) is calculated. The significance is assessed in a permutation test usingsignificance.permutation
withtestAlternative = "greater"
and free parameternumSamples, labs
. -
analysis.overrepresentation
calculates an overrepresentation analysis using the gene-level statisticgls.tStatistic
with parameterspValue
(should beTRUE
),alternative
andlabs
. The resulting values are then transformed viatransformation.adjustAndBinarize
(parameters are theadjMethod
andthreshold
). Finallygss.fisherExactTest
is used as gene set statistic. -
analysis.customOverrepresentation
calculates an overrepresentation analysis using a user-defined core setcoreSet
. That is, instead of calculating this core set internally based on differential expression as the standard overrepresentation analysis, this function allows for defining custom core sets. It internally uses the global analysisglobal.overrepresentation
. -
analysis.averageCorrelation
calculates the gene-level statistic as the absolute correlation usinggls.cor
(with parameterslabs, method
) andtransformation.abs
. The gene set statistic is the mean correlation calculated bygss.mean
. The significance is assessed by comparing the gene set statistic to randomly sampled gene sets usingsignificance.sampling
(with the parameternumSamples
and the preset parametertestAlternative = "greater"
). -
analysis.averageTStatistic
uses the absolute t statistic as the gene-level statistic by applyinggls.tStatistic
(with parameterslabs, pValue, alternative
) andtransformation.abs
. The gene set statistic is the mean t statistic in the gene set as returned bygss.mean
. The significance is assessed by comparing the gene set statistic to randomly sampled gene sets usingsignificance.sampling
(with the parameternumSamples
and the preset parametertestAlternative = "greater"
). -
analysis.globalTest
performs a global gene set enrichment analysis by Goeman et al. by applying theglobal.test
function which in turn wraps thegt
function in the globaltest package. -
analysis.globalAncova
applies the global ANCOVA method by Hummel et al. using the global methodglobal.ancova
which wraps theGlobalAncova
function in the GlobalAncova package.
Value
All functions return an object of class gsAnalysis
that specifies the corresponding analysis parameters for geneSetAnalysis
.
References
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., Mesirov, J. P. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Science of the United States of America, 102, 15545–15550.
Hummel, M., Meister, R., Mansmann, U. (2008) GlobalANCOVA: exploration and assessment of gene group effects. Bioinformatics, 24(1), 78–85.
Goeman, J. J., van de Geer, S. A., de Kort, F., van Houwelingen, H. C. (2004) A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20(1), 93–99.
See Also
Examples
data(exampleData)
# apply a gene set analysis based on the average absolute correlation
resAvCor <- geneSetAnalysis(
# parameters for geneSetAnalysis
dat = countdata,
geneSets = pathways[1],
analysis = analysis.averageCorrelation(),
adjustmentMethod = "fdr",
# additional parameters for analysis.averageCorrelation
labs = labels,
method = "pearson",
numSamples = 10)
# apply an overrepresentation analysis
resOverrep <- geneSetAnalysis(
# parameters for geneSetAnalysis
dat = countdata,
geneSets = pathways,
analysis = analysis.overrepresentation(),
adjustmentMethod = "fdr",
# additional parameters for analysis.overrepresentation
pValue = TRUE,
threshold = 0.1,
labs = labels
)
# apply a global analysis using GlobalAncova
resGA <- geneSetAnalysis(
# parameters for geneSetAnalysis
dat = countdata,
geneSets = pathways[1],
analysis = analysis.globalAncova(),
adjustmentMethod = "fdr",
# additional parameters for analysis.globalAncova
labs = labels,
method = "approx")