References

If you use PyCytoData to perform DR, citing the [our DR Review paper](https://doi.org/10.1101/2022.04.26.489549) is highly appreciated:

@article {Wang2022.04.26.489549,
        author = {Wang, Kaiwen and Yang, Yuqiu and Wu, Fangjiang and Song, Bing and Wang, Xinlei and Wang, Tao},
        title = {Comparative Analysis of Dimension Reduction Methods for Cytometry by Time-of-Flight Data},
        elocation-id = {2022.04.26.489549},
        year = {2022},
        doi = {10.1101/2022.04.26.489549},
        publisher = {Cold Spring Harbor Laboratory},
        URL = {https://www.biorxiv.org/content/early/2022/06/02/2022.04.26.489549},
        eprint = {https://www.biorxiv.org/content/early/2022/06/02/2022.04.26.489549.full.pdf},
        journal = {bioRxiv}
}

If you use Cytomulate with this package, [our paper](https://doi.org/10.1101/2022.06.14.496200) can be cited here:

@article {Yang2022.06.14.496200,
        author = {Yang, Yuqiu and Wang, Kaiwen and Lu, Zeyu and Wang, Tao and Wang, Xinlei},
        title = {Cytomulate: Accurate and Efficient Simulation of CyTOF data},
        elocation-id = {2022.06.14.496200},
        year = {2022},
        doi = {10.1101/2022.06.14.496200},
        publisher = {Cold Spring Harbor Laboratory},
        URL = {https://www.biorxiv.org/content/early/2022/06/16/2022.06.14.496200},
        eprint = {https://www.biorxiv.org/content/early/2022/06/16/2022.06.14.496200.full.pdf},
        journal = {bioRxiv}
}

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 hosts these datasets.

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