adjacency_permutation_test

adjacency_permutation_test(domain, network_name, label_name, population=None, include_boundaries=None, exclude_boundaries=None, boundary_exclude_distance=0, adjacency_order=1, label_shuffle_iterations=100, alpha=0.05, transform_counts=None, observation_threshold=15, visualise_output=False, visualise_correlation_matrix_kwargs={})

Test the correlation between adjacency labels in a network, also referred to as neighbourhood enrichment. This function computes the adjacency correlation matrix by considering the k-hop neighborhood of each object within the network by performing label shuffling to generate a null distribution and computing the standard effect size (SES) and corrected p-values (Benjamini-Hochberg) to identify significant adjacent label correlations.

Parameters:

domainobject

The domain object containing networks and labels.

network_namestr, optional

The name of the network to be analysed. Default is None.

label_namestr, optional

The name of the label to be analysed. Default is None.

populationarray-like or query, optional

A query or array of object indices to filter the labels. Default is None.

include_boundariesarray-like, query-like, or None, optional

Boundaries to include in the analysis. Defaults to None.

exclude_boundariesarray-like, query-like, or None, optional

Boundaries to exclude from the analysis. Defaults to None.

boundary_exclude_distancefloat, optional

Buffer to exclude objects located within boundary_exclude_distance from the boundaries. Defaults to 0.

adjacency_orderint, optional

The order of adjacency to consider for neighborhood. Default is 1.

label_shuffle_iterationsint, optional

The number of iterations for label shuffling. Default is 100.

alphafloat, optional

The significance level for p-value correction. Default is 0.05.

transform_countsstr, optional

The transformation to apply to the counts. Options are ‘arcsinh’, ‘log’, ‘sqrt’, or None. Default is None.

observation_thresholdint, optional

The minimum number of observations for a label in the network to be considered. Default is 15.

visualise_outputbool, optional

Whether to visualise the adjacency correlation matrix. Default is False.

visualise_correlation_matrix_kwargsdict, optional

Additional keyword arguments for visualising the correlation matrix.

Returns:

SESndarray

The standardized effect size matrix. Ordering of labels is the same as unique_labels_following_query.

Andarray

The p-value matrix. Ordering of labels is the same as unique_labels_following_query.

unique_labels_following_queryndarray

The unique labels considered after applying the query.

Raises:

RuntimeError

If the network name or label name is not found in the domain.

ValueError

If the label is not categorical or if the query type is incorrect.