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, retain_genes=None, layers_normalize=None, copy=False, **kwargs)

Filtering, normalization and log transform.

Expects non-logarithmized data.

Runs the following steps

scv.pp.filter_genes(adata)
scv.pp.normalize_per_cell(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).

• retain_genes (list, optional (default: None)) – List of gene names to be retained independent of thresholds.

• 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).

Return type:

Returns or updates adata depending on copy.