immuneML data model
=====================


.. meta::

   :twitter:card: summary
   :twitter:site: @immuneml
   :twitter:title: immuneML dev docs: data model
   :twitter:image: https://docs.immuneml.uio.no/_images/data_model_architecture.png

immuneML data model supports three types of datasets that can be used for analyses:

#. Repertoire dataset (:py:obj:`~immuneML.data_model.dataset.RepertoireDataset.RepertoireDataset`) - one example in the dataset is one repertoire typically coming from one subject
#. Receptor dataset (:py:obj:`~immuneML.data_model.dataset.ReceptorDataset.ReceptorDataset`) - one example is one receptor with both chains set
#. Sequence dataset (:py:obj:`~immuneML.data_model.dataset.SequenceDataset.SequenceDataset`) - one example is one receptor sequence with single chain information.

Useful function in the dataset classes include getting the metadata information from the :py:obj:`~immuneML.data_model.dataset.RepertoireDataset.RepertoireDataset`,
using :py:obj:`~immuneML.data_model.dataset.RepertoireDataset.RepertoireDataset.get_metadata` function, obtaining the number of examples in the
dataset, checking possible labels or making subsets.

The UML diagram showing these classes and the underlying dependencies is shown below.

.. figure:: ../_static/images/dev_docs/data_model_architecture.png
  :width: 70%

  UML diagram showing the immuneML data model, where white classes are abstract and define the interface only, while green are concrete and used throughout the codebase.

Implementation details for :code:`ReceptorDataset` and :code:`SequenceDataset` are available in :py:obj:`~immuneML.data_model.dataset.ElementDataset.ElementDataset`.