czbenchmarks.tasks.single_cell.cross_species
============================================

.. py:module:: czbenchmarks.tasks.single_cell.cross_species


Classes
-------

.. autoapisummary::

   czbenchmarks.tasks.single_cell.cross_species.CrossSpeciesIntegrationTask


Module Contents
---------------

.. py:class:: CrossSpeciesIntegrationTask(label_key: str)

   Bases: :py:obj:`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.

   :param label_key: Key to access ground truth cell type labels in metadata


   .. py:attribute:: label_key


   .. py:property:: display_name
      :type: str


      A pretty name to use when displaying task results


   .. py:property:: required_inputs
      :type: Set[czbenchmarks.datasets.DataType]


      Required input data types.

      :returns: Set of required input DataTypes (metadata with labels)


   .. py:property:: required_outputs
      :type: Set[czbenchmarks.datasets.DataType]


      Required output data types.

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


   .. py:property:: requires_multiple_datasets
      :type: bool


      Whether this task requires multiple datasets.

      :returns: True as this task compares data across species


   .. py:method:: set_baseline(data: List[czbenchmarks.datasets.SingleCellDataset], **kwargs)
      :abstractmethod:


      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.

      :param data: List of SingleCellDataset objects from different species
      :param \*\*kwargs: Additional arguments passed to run_standard_scrna_workflow

      :raises NotImplementedError: Always raised as baseline is not implemented