scvelo.pp.filter_and_normalize

scvelo.pp.filter_and_normalize(data, min_counts=None, min_counts_u=None, min_cells=None, min_cells_u=None, min_shared_counts=None, min_shared_cells=None, n_top_genes=None, flavor='seurat', log=True, copy=False)

Filtering, normalization and log transform

Expects non-logarithmized data. If using logarithmized data, pass log=False.

Runs the following steps

scv.pp.filter_genes(adata)
scv.pp.normalize_per_cell(adata)
if n_top_genes is not None:
    scv.pp.filter_genes_dispersion(adata)
if log:
    scv.pp.log1p(adata)
Parameters:
data : AnnData

Annotated data matrix.

min_counts : int (default: None)

Minimum number of counts required for a gene to pass filtering (spliced).

min_counts_u : int (default: None)

Minimum number of counts required for a gene to pass filtering (unspliced).

min_cells : int (default: None)

Minimum number of cells expressed required for a gene to pass filtering (spliced).

min_cells_u : int (default: None)

Minimum number of cells expressed required for a gene to pass filtering (unspliced).

min_shared_counts : int, optional (default: None)

Minimum number of counts (in cells expressed simultaneously in unspliced and spliced) required for a gene.

min_shared_cells : int, optional (default: None)

Minimum number of cells required for a gene to be expressed simultaneously in unspliced and spliced.

n_top_genes : int (default: None)

Number of genes to keep.

flavor : {'seurat', 'cell_ranger', 'svr'}, optional (default: 'seurat')

Choose the flavor for computing normalized dispersion. If choosing ‘seurat’, this expects non-logarithmized data.

log : bool (default: True)

Take logarithm.

copy : bool (default: False)

Return a copy of adata instead of updating it.

Returns:

Returns or updates adata depending on copy.