Description Usage Arguments Value Examples
Calculate cells gene specificty ranking and then perform geneset enrichment analysis on it.
1 2 3 4 5 6 7 8 9 10 11 12 13 | RunCellGSEA(X, pathways, reduction, dims, features, cells, nperm, minSize,
maxSize, gseaParam, n.core)
## S3 method for class 'Seurat'
RunCellGSEA(X, pathways, reduction = "mca",
dims = seq(50), features = NULL, cells = NULL, nperm = 1000,
minSize = 10, maxSize = 500, gseaParam = 0, n.core = 1)
## S3 method for class 'SingleCellExperiment'
RunCellGSEA(X, pathways,
reduction = "mca", dims = seq(50), features = NULL, cells = NULL,
nperm = 1000, minSize = 10, maxSize = 500, gseaParam = 0,
n.core = 1)
|
X |
Seurat or SingleCellExperiment object |
pathways |
List of gene sets to check |
reduction |
Which dimensionality reduction to use, must be based on MCA. |
dims |
A vector of integers indicating which dimensions to use with reduction embeddings and loadings for distance calculation. |
features |
Character vector of feature names to subset feature coordinates. If not specified will take all features available from specified reduction Loadings. |
cells |
Character vector of cell names to subset cell coordinates. If not specified will take all features available from specified reduction Embeddings |
nperm |
Number of permutations to do. Minimial possible nominal p-value is about 1/nperm |
minSize |
Minimal size of a gene set to test. All pathways below the threshold are excluded. |
maxSize |
Maximal size of a gene set to test. All pathways above the threshold are excluded. |
gseaParam |
GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores |
n.core |
A single integer to specify the number of core for parallelisation. |
A data.table with geneset enrichment analysis statistics.
1 2 | seuratPbmc <- RunMCA(seuratPbmc, nmcs = 5)
GSEAResults <- RunCellGSEA(seuratPbmc, Hallmark, dims = 1:5)
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