czbenchmarks.tasks.single_cell.cross_species

Classes

CrossSpeciesIntegrationTask

Task for evaluating cross-species integration quality.

Module Contents

class czbenchmarks.tasks.single_cell.cross_species.CrossSpeciesIntegrationTask(label_key: str, *, random_seed: int = RANDOM_SEED)

Bases: czbenchmarks.tasks.base.BaseTask

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.

Parameters:

• label_key – Key to access ground truth cell type labels in metadata

• random_seed (int) – Random seed for reproducibility

label_key

property display_name: str

A pretty name to use when displaying task results

property required_inputs: Set[czbenchmarks.datasets.DataType]

Required input data types.

Returns:

Set of required input DataTypes (metadata with labels)

property required_outputs: Set[czbenchmarks.datasets.DataType]

Required output data types.

Returns:

required output types from models this task to run (embedding coordinates)

property requires_multiple_datasets: bool

Whether this task requires multiple datasets.

Returns:

True as this task compares data across species

abstract set_baseline(data: List[czbenchmarks.datasets.SingleCellDataset], **kwargs)

Set a baseline embedding for cross-species integration.

This method is not implemented for cross-species integration tasks as standard preprocessing workflows are not directly applicable across different species.

Parameters:

• data – List of SingleCellDataset objects from different species

• **kwargs – Additional arguments passed to run_standard_scrna_workflow

Raises:

NotImplementedError – Always raised as baseline is not implemented