squidpy.gr.ligrec

squidpy.gr.ligrec(adata, cluster_key, interactions=None, complex_policy='min', threshold=0.01, corr_method=None, corr_axis='clusters', use_raw=True, copy=False, key_added=None, gene_symbols=None, *, n_perms=1000, rng=None, clusters=None, alpha=0.05, n_jobs=None, show_progress_bar=True, interactions_params=mappingproxy({}), transmitter_params=mappingproxy({'categories': 'ligand'}), receiver_params=mappingproxy({'categories': 'receptor'}), table_key=None)

Perform the permutation test as described in [Efremova et al., 2020].

Parameters:

• adata (AnnData | SpatialData) – Annotated data object.

• use_raw (bool) – Whether to access anndata.AnnData.raw.

• table_key (str | None) – Key in spatialdata.SpatialData.tables where the table is stored. Required when adata is a spatialdata.SpatialData object and ignored otherwise.

• interactions (DataFrame | Mapping[str, Sequence[str]] | Sequence[str] | tuple[Sequence[str], Sequence[str]] | Sequence[tuple[str, str]] | None) –

  Interaction to test. The type can be one of:

  • pandas.DataFrame - must contain at least 2 columns named ‘source’ and ‘target’.

  • dict - dictionary with at least 2 keys named ‘source’ and ‘target’.

  • typing.Sequence - Either a sequence of str, in which case all combinations are produced, or a sequence of tuple of 2 str or a tuple of 2 sequences.

  If None, the interactions are extracted from omnipath. Protein complexes can be specified by delimiting the components with ‘_’, such as ‘alpha_beta_gamma’.

• complex_policy (Literal['min', 'all']) –

  Policy on how to handle complexes. Valid options are:

  • ’min’ - select gene with the minimum average expression. This is the same as in [Efremova et al., 2020].

  • ’all’ - select all possible combinations between ‘source’ and ‘target’ complexes.

• interactions_params (Mapping[str, Any]) – Keyword arguments for omnipath.interactions.import_intercell_network() defining the interactions. These datasets from [Türei et al., 2016] are used by default: omnipath, pathwayextra, kinaseextra and ligrecextra.

• transmitter_params (Mapping[str, Any]) – Keyword arguments for omnipath.interactions.import_intercell_network() defining the transmitter side of intercellular connections.

• receiver_params (Mapping[str, Any]) – Keyword arguments for omnipath.interactions.import_intercell_network() defining the receiver side of intercellular connections.

• cluster_key (str) – Key in anndata.AnnData.obs where clustering is stored.

• clusters (Sequence[str] | tuple[Sequence[str], Sequence[str]] | Sequence[tuple[str, str]] | None) – Clusters from anndata.AnnData.obs ['{cluster_key}']. Can be specified either as a sequence of tuple or just a sequence of cluster names, in which case all combinations considered.

• n_perms (int) – Number of permutations for the permutation test.

• threshold (float) – Do not perform permutation test if any of the interacting components is being expressed in less than threshold percent of cells within a given cluster.

• rng (int | integer | Sequence[int] | SeedSequence | Generator | BitGenerator | None) – Pseudorandom number generator state, following SPEC 7. When None, a new numpy.random.Generator is created using entropy from the operating system. Types other than numpy.random.Generator are passed to numpy.random.default_rng() to instantiate a generator.

• corr_method (str | None) – Correction method for multiple testing. See statsmodels.stats.multitest.multipletests() for valid options.

• corr_axis (Literal['interactions', 'clusters']) –

  Axis over which to perform the FDR correction. Only used when corr_method != None. Valid options are:

  • ’interactions’ - correct interactions by performing FDR correction across the clusters.

  • ’clusters’ - correct clusters by performing FDR correction across the interactions.

• alpha (float) – Significance level for FDR correction. Only used when corr_method != None.

• copy (bool) – If True, return the result, otherwise save it to the adata object.

• key_added (str | None) – Key in anndata.AnnData.uns where the result is stored if copy = False. If None, '{cluster_key}_ligrec' will be used.

• n_jobs (int | None) – Number of numba threads used for the permutations. Peak memory grows linearly with it. If None, numba’s default thread count (usually all cores) is used; negative values count down from the maximum.

• show_progress_bar (bool) – Whether to show the progress bar or not.

• gene_symbols (str | None) – Key in anndata.AnnData.var to use instead of anndata.AnnData.var_names.

Return type:

Mapping[str, DataFrame] | None

Returns:

If copy = True, returns a dict with following keys:

• ’means’ - pandas.DataFrame containing the mean expression.

• ’pvalues’ - pandas.DataFrame containing the possibly corrected p-values.

• ’metadata’ - pandas.DataFrame containing interaction metadata.

Otherwise, modifies the adata object with the following key:

• anndata.AnnData.uns ['{key_added}'] - the above mentioned dict.

NaN p-values mark combinations for which the mean expression of one of the interacting components was 0 or it didn’t pass the threshold percentage of cells being expressed within a given cluster.