Subset Seurat Objects

Subset Seurat Objects

# S3 method for class 'Seurat'
subset(
  x,
  subset,
  cells = NULL,
  features = NULL,
  idents = NULL,
  return.null = FALSE,
  ...
)

# S3 method for class 'Seurat'
x[i, j, ...]

Arguments

x

A Seurat object

subset

Logical expression indicating features/variables to keep

cells, j

A vector of cell names or indices to keep

features, i

A vector of feature names or indices to keep

idents

A vector of identity classes to keep

return.null

If no cells are requested, return a NULL; by default, throws an error

...

Arguments passed to WhichCells

Value

subset: A subsetted Seurat object

[: object x with features i and cells j

See also

WhichCells

Seurat object, validity, and interaction methods $.Seurat(), Seurat-class, Seurat-validity, [[.Seurat(), [[<-,Seurat, [[<-,Seurat,NULL, dim.Seurat(), dimnames.Seurat(), merge.Seurat(), names.Seurat()

Examples

# `subset` examples
subset(pbmc_small, subset = MS4A1 > 4)
#> An object of class Seurat 
#> 230 features across 10 samples within 1 assay 
#> Active assay: RNA (230 features, 20 variable features)
#>  3 layers present: counts, data, scale.data
#>  2 dimensional reductions calculated: pca, tsne
subset(pbmc_small, subset = `DLGAP1-AS1` > 2)
#> An object of class Seurat 
#> 230 features across 4 samples within 1 assay 
#> Active assay: RNA (230 features, 20 variable features)
#>  3 layers present: counts, data, scale.data
#>  2 dimensional reductions calculated: pca, tsne
subset(pbmc_small, idents = '0', invert = TRUE)
#> An object of class Seurat 
#> 230 features across 44 samples within 1 assay 
#> Active assay: RNA (230 features, 20 variable features)
#>  3 layers present: counts, data, scale.data
#>  2 dimensional reductions calculated: pca, tsne
subset(pbmc_small, subset = MS4A1 > 3, slot = 'counts')
#> An object of class Seurat 
#> 230 features across 3 samples within 1 assay 
#> Active assay: RNA (230 features, 20 variable features)
#>  3 layers present: counts, data, scale.data
#>  2 dimensional reductions calculated: pca, tsne
subset(pbmc_small, features = VariableFeatures(object = pbmc_small))
#> An object of class Seurat 
#> 20 features across 80 samples within 1 assay 
#> Active assay: RNA (20 features, 20 variable features)
#>  3 layers present: counts, data, scale.data
#>  2 dimensional reductions calculated: pca, tsne

# `[` examples
pbmc_small[VariableFeatures(object = pbmc_small), ]
#> An object of class Seurat 
#> 20 features across 80 samples within 1 assay 
#> Active assay: RNA (20 features, 20 variable features)
#>  3 layers present: counts, data, scale.data
#>  2 dimensional reductions calculated: pca, tsne
pbmc_small[, 1:10]
#> An object of class Seurat 
#> 230 features across 10 samples within 1 assay 
#> Active assay: RNA (230 features, 20 variable features)
#>  3 layers present: counts, data, scale.data
#>  2 dimensional reductions calculated: pca, tsne