czbenchmarks.tasks.single_cell

Submodules

• czbenchmarks.tasks.single_cell.cross_species
• czbenchmarks.tasks.single_cell.perturbation

Classes

CrossSpeciesIntegrationTask	Task for evaluating cross-species integration quality.
PerturbationTask	Task for evaluating perturbation prediction quality.

Package Contents

class czbenchmarks.tasks.single_cell.CrossSpeciesIntegrationTask(label_key: str)

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

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

class czbenchmarks.tasks.single_cell.PerturbationTask

Bases: czbenchmarks.tasks.base.BaseTask

Task for evaluating perturbation prediction quality.

This task computes metrics to assess how well a model predicts gene expression changes in response to perturbations. Compares predicted vs ground truth perturbation effects using MSE and correlation metrics.

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 (ground truth perturbation effects)

property required_outputs: Set[czbenchmarks.datasets.DataType]

Required output data types.

Returns:

required output types from models this task to run (predicted perturbation effects)

set_baseline(data: czbenchmarks.datasets.PerturbationSingleCellDataset, gene_pert: str, baseline_type: Literal['median', 'mean'] = 'median', **kwargs)

Set a baseline embedding for perturbation prediction.

Creates baseline predictions using simple statistical methods (median and mean) applied to the control data, and evaluates these predictions against ground truth.

Parameters:

• data – PerturbationSingleCellDataset containing control and perturbed data

• gene_pert – The perturbation gene to evaluate

• baseline_type – The statistical method to use for baseline prediction (median or mean)

• **kwargs – Additional arguments passed to the evaluation

Returns:

List of MetricResult objects containing baseline performance metrics for different statistical methods (median, mean)