liana.method.bivariate.__call__

liana.method.bivariate.__call__#

bivariate.__call__(local_name='cosine', global_name=None, resource_name=None, resource=None, interactions=None, connectivity_key='spatial_connectivities', mask_negatives=False, add_categories=False, n_perms=None, seed=1337, nz_prop=0.05, remove_self_interactions=True, complex_sep='_', xy_sep='^', verbose=False, **kwargs)#

A method for bivariate local spatial metrics.

Parameters:

  • mdata (MuData | AnnData) – MuData (multimodal) data object.

  • local_name (str | None (default: 'cosine')) – Name of the local function to use for the analysis. Passing None will return only the Global scores.

  • global_name (None | str | list[str] (default: None)) – Name or names (list) of the global function(s) to use for the analysis. Passing None will not calculate any global scores

  • resource_name (str (default: None)) – Name of the resource to be used for ligand-receptor inference. See li.rs.show_resources() for available resources.

  • resource (DataFrame | None (default: None)) – A pandas dataframe with [ligand, receptor] columns. If provided will overrule the resource requested via resource_name

  • interactions (list[str] (default: None)) – List of tuples with ligand-receptor pairs [(ligand, receptor), ...] to be used for the analysis. If passed, it will overrule the resource requested via resource and resource_name.

  • connectivity_key (str (default: 'spatial_connectivities')) – Key in adata.obsp that contains the spatial connectivity matrix. Default is 'spatial_connectivity'.

  • mask_negatives (bool (default: False)) – Whether to mask negative-negative (low-low) or uncategorized interactions.

  • add_categories (bool (default: False)) – Whether to add categories about the local scores.

  • n_perms (int (default: None)) – Number of permutations for the permutation test. If None, no p-values are computed.

  • seed (int (default: 1337)) – Random seed for reproducibility.

  • nz_prop (float (default: 0.05)) – Minimum proportion of non-zero values for each features. For example, if working with gene expression data, this would be the proportion of cells expressing a gene. Both features must have a proportion greater than nz_prop to be considered in the analysis.

  • remove_self_interactions (bool (default: True)) – Whether to remove self-interactions. True by default.

  • complex_sep (None | str (default: '_')) – Separator to use for complex names.

  • xy_sep (str (default: '^')) – Separator to use for interaction names.

  • verbose (bool (default: False)) – Verbosity flag.

  • **kwargs –

    Additional keyword arguments: - For AnnData:

    x_name

    Name of the x-variable. If passing a resource dataframe, this should match the first column. By default: ‘ligand’.

    y_name

    Name of the y-variable. If passing a resource dataframe, this should match the second column. By default: ‘receptor’.

    • For MuData:

      x_mod

    Name of the modality to use for the x-axis.

    y_mod

    Name of the modality to use for the y-axis.

    x_name

    Name of the x-variable. If passing a resource dataframe, this should match the first column. By default: ‘x’.

    y_name

    Name of the y-variable. If passing a resource dataframe, this should match the second column. By default: ‘y’.
    x_use_raw: bool

    Whether to use the raw counts for the x-mod.

    y_use_raw: bool

    Whether to use the raw counts for y-mod.

    x_layer: str

    Layer to use for x-mod.

    y_layer: str

    Layer to use for y-mod.

    x_transform: bool

    Function to transform the x-mod.

    y_transform: bool

    Function to transform the y-mod.

Raises:

ValueError – If n_perms is not None or negative or if mdata is not a valid type.

Return type:

AnnData | DataFrame | None

Returns:

An AnnData object, (optionally) with multiple layers which correspond categories/p-values, and the actual scores are stored in .X. Moreover, global stats are stored in .var.