mechanochat.preprocessing.filter_lr_database#

mechanochat.preprocessing.filter_lr_database(df_ligrec: DataFrame, adata: AnnData, heteromeric: bool = True, heteromeric_delimiter: str = '_', heteromeric_rule: Literal['min', 'ave'] = 'min', filter_criteria: Literal['min_cell_pct', 'min_cell'] = 'min_cell_pct', min_cell: int = 100, min_cell_pct: float = 0.05) → DataFrame[source]#

Filter ligand-receptor pairs.

Parameters:

  • df_ligrec (pandas.DataFrame) – The pandas dataframe of ligand-receptor database with three columns being ligand, receptor, and pathway name respectively.

  • adata (Anndata) – The AnnData object of gene expression. Unscaled data (minimum being zero) is expected.

  • heteromeric (bool) – Whether the ligands and receptors are described as heteromeric.

  • heteromeric_delimiter (str) – If heteromeric notations are used for ligands and receptors, the character separating the heteromeric units.

  • heteromeric_rule (str) – When heteromeric is True, the rule to quantify the level of a heteromeric ligand or receptor. Choose from minimum (‘min’) and average (‘ave’).

  • filter_criteria (str) – Use either cell percentage (‘min_cell_pct’) or cell numbers (min_cell) to filter genes.

  • min_cell (int) – If filter_criteria is ‘min_cell’, the LR-pairs with both ligand and receptor detected in greater than or equal to min_cell cells are kept.

  • min_cell_pct (float) – If filter_criteria is ‘min_cell_pct’, the LR-pairs with both ligand and receptor detected in greater than or equal to min_cell_pct percentage of cells are kepts.

Returns:

df_ligrec_filtered (pandas.DataFrame) – A pandas DataFrame of the filtered ligand-receptor pairs. Columns: ‘Ligands’, ‘Receptor’, ‘Pathway’, ‘Type’

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