Description Usage Details Value References See Also Examples
Predefined analysis configurations that can be used in geneSetAnalysis
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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 by gls.cor
with the associated parameters labs, method
and a preprocessing by transformation.abs
. As a gene set statistic, the enrichment score (function gss.enrichmentScore
with parameter p
) is calculated. The significance is assessed in a permutation test using significance.permutation
with testAlternative = "greater"
and free parameter numSamples, labs
.
analysis.overrepresentation
calculates an overrepresentation analysis using the gene-level statistic gls.tStatistic
with parameters pValue
(should be TRUE
), alternative
and labs
. The resulting values are then transformed via transformation.adjustAndBinarize
(parameters are the adjMethod
and threshold
). Finally gss.fisherExactTest
is used as gene set statistic.
analysis.customOverrepresentation
calculates an overrepresentation analysis using a user-defined core set coreSet
. 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 analysis global.overrepresentation
.
analysis.averageCorrelation
calculates the gene-level statistic as the absolute correlation using gls.cor
(with parameters labs, method
) and transformation.abs
. The gene set statistic is the mean correlation calculated by gss.mean
. The significance is assessed by comparing the gene set statistic to randomly sampled gene sets using significance.sampling
(with the parameter numSamples
and the preset parameter testAlternative = "greater"
).
analysis.averageTStatistic
uses the absolute t statistic as the gene-level statistic by applying gls.tStatistic
(with parameters labs, pValue, alternative
) and transformation.abs
. The gene set statistic is the mean t statistic in the gene set as returned by gss.mean
. The significance is assessed by comparing the gene set statistic to randomly sampled gene sets using significance.sampling
(with the parameter numSamples
and the preset parameter testAlternative = "greater"
).
analysis.globalTest
performs a global gene set enrichment analysis by Goeman et al. by applying the global.test
function which in turn wraps the gt
function in the globaltest package.
analysis.globalAncova
applies the global ANCOVA method by Hummel et al. using the global method global.ancova
which wraps the GlobalAncova
function in the GlobalAncova package.
All functions return an object of class gsAnalysis
that specifies the corresponding analysis parameters for geneSetAnalysis
.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | # load data
require(GlobalAncova)
data(vantVeer)
data(phenodata)
data(pathways)
# apply a gene set analysis based on the average absolute correlation
resAvCor <- geneSetAnalysis(
# parameters for geneSetAnalysis
dat = vantVeer,
geneSets = pathways[1],
analysis = analysis.averageCorrelation(),
adjustmentMethod = "fdr",
# additional parameters for analysis.averageCorrelation
labs = phenodata$metastases,
method = "pearson",
numSamples = 10)
# apply an overrepresentation analysis
resOverrep <- geneSetAnalysis(
# parameters for geneSetAnalysis
dat = vantVeer,
geneSets = pathways,
analysis = analysis.overrepresentation(),
adjustmentMethod = "fdr",
# additional parameters for analysis.overrepresentation
pValue = TRUE,
threshold = 0.1,
labs = phenodata$metastases
)
# apply a global analysis using GlobalAncova
resGA <- geneSetAnalysis(
# parameters for geneSetAnalysis
dat = vantVeer,
geneSets = pathways[1],
analysis = analysis.globalAncova(),
adjustmentMethod = "fdr",
# additional parameters for analysis.globalAncova
labs = phenodata$metastases,
method = "approx")
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