crested.pp.sort_and_filter_regions_on_specificity

crested.pp.sort_and_filter_regions_on_specificity#

crested.pp.sort_and_filter_regions_on_specificity(adata, top_k, model_name=None, method='proportion', min_score=None, inplace=True)#

Sort bed regions & targets/predictions based on high Gini or proportion score per column while keeping the top k rows per column.

Combines them into a single AnnData object with extra columns indicating the original class name, the rank per column, and the score. To get an idea for the impact of different possible top_k and min_score values, see sort_and_filter_cutoff(). We highly recommend setting a min_score value especially when using “gini” on a small dataset.

Parameters:

  • adata (AnnData) – The AnnData object containing the matrix (celltypes, regions) to be sorted.

  • top_k (int) – The number of top regions to keep per column.

  • model_name (str | None (default: None)) – The name of the model to look for in adata.layers[model_name] for predictions. If None, will use the targets in adata.X to decide which regions to sort.

  • method (Literal['gini', 'proportion'] (default: 'proportion')) – The method to use for calculating scores, either ‘gini’ or ‘proportion’. Default is ‘proportion’ since v1.10.0, ‘gini’ before that.

  • min_score (float | None (default: None)) – If provided, keep only regions with gini or proportion score to be greater than this value. Will potentially lead to unbalanced numbers of regions by classes.

  • inplace (bool (default: True)) – Perform computation and modify adata in-place or return a resulting copy of the adata instead.

Return type:

AnnData | None

Returns:

If inplace=True (default), returns nothing and modifies the AnnData in-place with the sorted and filtered matrix, and extra columns indicating the original class name, the rank per column, and the score. If inplace=False, returns a modified copy of the AnnData object instead.

See also

crested.pl.qc.sort_and_filter_cutoff

Example

>>> crested.pp.sort_and_filter_regions_on_specificity(
...     adata,
...     top_k=500,
...     method="proportion",
... )