Find cells with highest scores for a given dimensional reduction technique

Return a list of genes with the strongest contribution to a set of components

TopCells(object, dim = 1, ncells = 20, balanced = FALSE, ...)

Arguments

object	DimReduc object
dim	Dimension to use
ncells	Number of cells to return
balanced	Return an equal number of cells with both + and - scores.
...	Extra parameters passed to `Embeddings`

Value

Returns a vector of cells

Examples

data("pbmc_small")
pbmc_small
#> An object of class Seurat 
#> 230 features across 80 samples within 1 assay 
#> Active assay: RNA (230 features, 20 variable features)
#>  2 dimensional reductions calculated: pca, tsne
head(TopCells(object = pbmc_small[["pca"]]))
#> [1] "ACGTGATGCCATGA" "ATACCACTCTAAGC" "ATTGCACTTGCTTT" "CTAGGTGATGGTTG"
#> [5] "GACATTCTCCACCT" "ATAGGAGAAACAGA"
# Can specify which dimension and how many cells to return
TopCells(object = pbmc_small[["pca"]], dim = 2, ncells = 5)
#> [1] "ACAGGTACTGGTGT" "GTTGACGATATCGG" "GGCATATGCTTATC" "CTAACGGAACCGAT"
#> [5] "CATTACACCAACTG"