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, retain_genes=None, subset_highly_variable=True, flavor='seurat', log=True, layers_normalize=None, copy=False, **kwargs)¶

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 to pass filtering (spliced).
min_cells_u : int (default: None): Minimum number of cells expressed required to pass filtering (unspliced).
min_shared_counts : int, optional (default: None): Minimum number of counts (both unspliced and spliced) required for a gene.
min_shared_cells : int, optional (default: None): Minimum number of cells required to be expressed (both unspliced and spliced).
n_top_genes : int (default: None): Number of genes to keep.
retain_genes : list, optional (default: None): List of gene names to be retained independent of thresholds.
subset_highly_variable : bool (default: True): Whether to subset highly variable genes or to store in .var[‘highly_variable’].
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.
layers_normalize : list of str (default: None): List of layers to be normalized. If set to None, the layers {‘X’, ‘spliced’, ‘unspliced’} are considered for normalization upon testing whether they have already been normalized (by checking type of entries: int -> unprocessed, float -> processed).
copy : bool (default: False): Return a copy of adata instead of updating it.
**kwargs: Keyword arguments passed to pp.normalize_per_cell (e.g. counts_per_cell).

Returns:

Returns or updates adata depending on copy.