################
References
################

If you used ``PyCytoData`` as part of your research or used ``Cytomulate`` with this package, `our paper <https://doi.org/10.1186/s13059-023-03099-1>`_
can be cited here:

.. code-block::

	Yang, Y., Wang, K., Lu, Z. et al. Cytomulate: accurate and efficient simulation of CyTOF data. Genome Biol 24, 262 (2023). https://doi.org/10.1186/s13059-023-03099-1

or

.. code-block:: 

	@article {Yang2023,
		author = {Yang, Yuqiu and Wang, Kaiwen and Lu, Zeyu and Wang, Tao and Wang, Xinlei},
		title = {Cytomulate: accurate and efficient simulation of CyTOF data},
		journal={Genome biology},
		volume={24},
		number={262},
		year={2023},
		publisher={Springer}
	}



If you use ``PyCytoData`` to perform DR with ``CytofDR``, citing the `our DR Review paper <https://doi.org/10.1038/s41467-023-37478-w>`_ is highly appreciated:

.. code-block::

	Wang, K., Yang, Y., Wu, F. et al. Comparative analysis of dimension reduction methods for cytometry by time-of-flight data. Nat Commun 14, 1836 (2023).
	https://doi.org/10.1038/s41467-023-37478-w


or


.. code-block::

	@article{wang2023comparative,
		title={Comparative analysis of dimension reduction methods for cytometry by time-of-flight data},
		author={Wang, Kaiwen and Yang, Yuqiu and Wu, Fangjiang and Song, Bing and Wang, Xinlei and Wang, Tao},
		journal={Nature communications},
		volume={14},
		number={1},
		pages={1--18},
		year={2023},
		publisher={Nature Publishing Group UK London}
	}


If you use the builtin datasets, please visit our `Reference Page <https://pycytodata.readthedocs.io/en/latest/references.html>`_ and cite the papers accordingly.

--------------------------

********************
Benchmark Datasets
********************

If you use the builtin datasets (``levine13``, ``levine32``, ``samusik``), you can cite the following papers
along with ``HDCytoData``, which serve as the inspiration for this package.

- Weber, L. M., & Soneson, C. (2019). HDCytoData: collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats. F1000Research, 8.
- Levine et al. (2015). Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell, 162, pp. 184-197. 
- Samusik et al. (2016), "Automated mapping of phenotype space with single-cell data", Nature Methods, 13(6), 493-496: https://www.ncbi.nlm.nih.gov/pubmed/27183440