GetCellGeneSet {CelliD} | R Documentation |
Calculate cells and genes distances, rank them per cell and extract top n features. The obtained top n features represents features thatare highly specific to that cell.
GetCellGeneSet(X, reduction = "mca", dims, features, cells, n.features) ## S3 method for class 'Seurat' GetCellGeneSet( X, reduction = "mca", dims = seq(50), features = NULL, cells = NULL, n.features = 200 ) ## S3 method for class 'SingleCellExperiment' GetCellGeneSet( X, reduction = "MCA", dims = seq(50), features = NULL, cells = NULL, n.features = 200 )
X |
Seurat or SingleCell Experiment Object |
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 Embeddigns. |
n.features |
single integer specifying how many top features should be extracted from the ranking |
A cell named list of gene rankings ordererd by distances from shortest (most specfic) to farthest (less specific)
seuratPbmc <- RunMCA(seuratPbmc, nmcs = 5) GroupGeneRanking <- GetGroupGeneRanking(seuratPbmc, group.by = "seurat_clusters", dims = 1:5)