czbenchmarks.tasks.single_cell.cross_species_integration

Attributes

logger

Classes

CrossSpeciesIntegrationTaskInput	Pydantic model for CrossSpeciesIntegrationTask inputs.
CrossSpeciesIntegrationOutput	Output for cross-species integration task.
CrossSpeciesIntegrationTask	Task for evaluating cross-species integration quality.

Module Contents

czbenchmarks.tasks.single_cell.cross_species_integration.logger

class czbenchmarks.tasks.single_cell.cross_species_integration.CrossSpeciesIntegrationTaskInput(/, **data: Any)

Bases: czbenchmarks.tasks.task.TaskInput

Pydantic model for CrossSpeciesIntegrationTask inputs.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

labels: Annotated[List[czbenchmarks.types.ListLike], Field(description='List of ground truth labels for each species dataset (e.g., cell types).')]

organism_list: Annotated[List[czbenchmarks.datasets.types.Organism], Field(description='List of organisms corresponding to each dataset for cross-species evaluation.')]

class czbenchmarks.tasks.single_cell.cross_species_integration.CrossSpeciesIntegrationOutput(/, **data: Any)

Bases: czbenchmarks.tasks.task.TaskOutput

Output for cross-species integration task.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

cell_representation: czbenchmarks.tasks.types.CellRepresentation

labels: czbenchmarks.types.ListLike

species: czbenchmarks.types.ListLike

class czbenchmarks.tasks.single_cell.cross_species_integration.CrossSpeciesIntegrationTask(*, random_seed: int = RANDOM_SEED)

Bases: czbenchmarks.tasks.task.Task

Task for evaluating cross-species integration quality.

This task computes metrics to assess how well different species’ data are integrated in the embedding space while preserving biological signals. It operates on multiple datasets from different species.

display_name = 'Cross-species Integration'

description = 'Evaluate cross-species integration quality using various integration metrics.'

input_model

baseline_model

requires_multiple_datasets = True

abstract compute_baseline(expression_data: czbenchmarks.tasks.types.CellRepresentation, baseline_input: czbenchmarks.tasks.task.NoBaselineInput = None)

Set a baseline embedding for cross-species integration.

Not implemented as standard preprocessing is not applicable across species.