scanpy.pp.filter_cells

scanpy.pp.filter_cells(data, min_counts=None, min_genes=None, max_counts=None, max_genes=None, inplace=True, copy=False)

Filter cell outliers based on counts and numbers of genes expressed.

For instance, only keep cells with at least min_counts counts or min_genes genes expressed. This is to filter measurement outliers, i.e. “unreliable” observations.

Only provide one of the optional parameters min_counts, min_genes, max_counts, max_genes per call.

Parameters

data : AnnDataAnnData: The (annotated) data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes.
min_counts : int | NoneOptional[int] (default: None): Minimum number of counts required for a cell to pass filtering.
min_genes : int | NoneOptional[int] (default: None): Minimum number of genes expressed required for a cell to pass filtering.
max_counts : int | NoneOptional[int] (default: None): Maximum number of counts required for a cell to pass filtering.
max_genes : int | NoneOptional[int] (default: None): Maximum number of genes expressed required for a cell to pass filtering.
inplace : boolbool (default: True): Perform computation inplace or return result.

Return type

Tuple[ndarray, ndarray] | NoneOptional[Tuple[ndarray, ndarray]]

Returns

Depending on inplace, returns the following arrays or directly subsets and annotates the data matrix:

cells_subsetndarrayndarray: Boolean index mask that does filtering. True means that the cell is kept. False means the cell is removed.
number_per_cellndarrayndarray: Depending on what was tresholded (counts or genes), the array stores n_counts or n_cells per gene.

Examples

>>> import scanpy as sc
>>> adata = sc.datasets.krumsiek11()
>>> adata.n_obs
640
>>> adata.var_names
['Gata2' 'Gata1' 'Fog1' 'EKLF' 'Fli1' 'SCL' 'Cebpa'
 'Pu.1' 'cJun' 'EgrNab' 'Gfi1']
>>> # add some true zeros
>>> adata.X[adata.X < 0.3] = 0
>>> # simply compute the number of genes per cell
>>> sc.pp.filter_cells(adata, min_genes=0)
>>> adata.n_obs
640
>>> adata.obs['n_genes'].min()
1
>>> # filter manually
>>> adata_copy = adata[adata.obs['n_genes'] >= 3]
>>> adata_copy.obs['n_genes'].min()
>>> adata.n_obs
554
>>> adata.obs['n_genes'].min()
3
>>> # actually do some filtering
>>> sc.pp.filter_cells(adata, min_genes=3)
>>> adata.n_obs
554
>>> adata.obs['n_genes'].min()
3