References

If you used PyCytoData as part of your research or used Cytomulate with this package, our paper can be cited here:

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

@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 is highly appreciated:

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

@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 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 serves 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