RunGSEA: Runs GSEA on the gene association coefficients for each NMF
In yanwu2014/swne: Similarity Weighted Nonnegative Embedding: A method for visualizing high dimensional datasets
RunGSEA R Documentation
Runs GSEA on the gene association coefficients for each NMF
Description
Runs GSEA on the gene association coefficients for each NMF
Usage
RunGSEA(gene.factor.assoc, genesets, power = 1, n.rand = 1000, n.cores = 1)
Arguments
gene.factor.assoc
Matrix with the gene associations for each nmf
genesets
Genesets to use (as a named list)
power
GSEA coefficient power
n.rand
Number of permutations to use when calculating significance
n.cores
Number of threads to use
Value
Dataframe summarizing the gsea results
yanwu2014/swne documentation built on Aug. 5, 2023, 4:42 a.m.
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| RunGSEA | R Documentation |
Runs GSEA on the gene association coefficients for each NMF
Description
Runs GSEA on the gene association coefficients for each NMF
Usage
RunGSEA(gene.factor.assoc, genesets, power = 1, n.rand = 1000, n.cores = 1)
Arguments
gene.factor.assoc |
Matrix with the gene associations for each nmf |
genesets |
Genesets to use (as a named list) |
power |
GSEA coefficient power |
n.rand |
Number of permutations to use when calculating significance |
n.cores |
Number of threads to use |
Value
Dataframe summarizing the gsea results
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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