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2 | 2 | References
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3 | 3 | ################
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4 | 4 |
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5 |
| -If you used ``PyCytoData`` as part of your research or used ``Cytomulate`` with this package, `our paper <https://doi.org/10.1101/2022.06.14.496200>`_ |
| 5 | +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>`_ |
6 | 6 | can be cited here:
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7 | 7 |
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| 8 | +.. code-block:: |
| 9 | +
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| 10 | + 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 |
| 11 | +
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| 12 | +or |
| 13 | + |
8 | 14 | .. code-block::
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9 | 15 |
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10 |
| - @article {Yang2022.06.14.496200, |
| 16 | + @article {Yang2023, |
11 | 17 | author = {Yang, Yuqiu and Wang, Kaiwen and Lu, Zeyu and Wang, Tao and Wang, Xinlei},
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12 |
| - title = {Cytomulate: Accurate and Efficient Simulation of CyTOF data}, |
13 |
| - elocation-id = {2022.06.14.496200}, |
14 |
| - year = {2022}, |
15 |
| - doi = {10.1101/2022.06.14.496200}, |
16 |
| - publisher = {Cold Spring Harbor Laboratory}, |
17 |
| - URL = {https://www.biorxiv.org/content/early/2022/06/16/2022.06.14.496200}, |
18 |
| - eprint = {https://www.biorxiv.org/content/early/2022/06/16/2022.06.14.496200.full.pdf}, |
19 |
| - journal = {bioRxiv} |
| 18 | + title = {Cytomulate: accurate and efficient simulation of CyTOF data}, |
| 19 | + journal={Genome biology}, |
| 20 | + volume={24}, |
| 21 | + number={262}, |
| 22 | + year={2023}, |
| 23 | + publisher={Springer} |
20 | 24 | }
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21 | 25 |
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22 | 26 |
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@@ -55,7 +59,7 @@ Benchmark Datasets
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55 | 59 | ********************
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56 | 60 |
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57 | 61 | If you use the builtin datasets (``levine13``, ``levine32``, ``samusik``), you can cite the following papers
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58 |
| -along with ``HDCytoData``, which hosts these datasets. |
| 62 | +along with ``HDCytoData``, which serves as the inspiration for this package. |
59 | 63 |
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60 | 64 | - Weber, L. M., & Soneson, C. (2019). HDCytoData: collection of high-dimensional cytometry benchmark datasets in Bioconductor object formats. F1000Research, 8.
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61 | 65 | - Levine et al. (2015). Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell, 162, pp. 184-197.
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