pyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells.
pyInfinityFlow: optimized imputation and analysis of high-dimensional flow cytometry data for millions of cells.
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DOI：
10.1093/bioinformatics/btad287
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发表时间：
2023-05-04
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虽然传统的流式细胞术仅限于数十个标记，但新的实验和计算策略，如Infinity flow，允许在数百万个细胞中生成和植入数百个细胞表面蛋白标记。在这里，我们用Python描述了Infinity Flow数据的端到端分析工作流。pyInfinityFlow通过与成熟的Python包直接集成进行单细胞基因组学分析，可以有效地分析数百万个细胞，而无需降低采样。pyInfinityFlow准确地识别常见和极其罕见的细胞群，这些细胞群很难单独从单细胞基因组学研究中定义。我们证明这种工作流程可以提名新的标记物来设计新的流式细胞术门控策略来预测细胞群。pyInfinityFlow可以扩展到不同的细胞发现分析，灵活地适应不同的InfinityFlow实验设计。pyInfinityFlow在GitHub （https://github.com/KyleFerchen/pyInfinityFlow）和PyPI （https://pypi.org/project/pyInfinityFlow/）中免费提供。带有测试数据集教程的包文档可通过阅读文档（pyinfinityflow.readthedocs.io）获得。用于重现结果的脚本和数据可在https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analysis_scripts上获得，以及原始流式细胞术输入数据。
While conventional flow cytometry is limited to dozens of markers, new experimental and computational strategies, such as Infinity Flow, allow for the generation and imputation of hundreds of cell surface protein markers in millions of cells. Here, we describe an end-to-end analysis workflow for Infinity Flow data in Python. pyInfinityFlow enables the efficient analysis of millions of cells, without down-sampling, through direct integration with well-established Python packages for single-cell genomics analysis. pyInfinityFlow accurately identifies both common and extremely rare cell populations which are challenging to define from single-cell genomics studies alone. We demonstrate that this workflow can nominate novel markers to design new flow cytometry gating strategies for predicted cell populations. pyInfinityFlow can be extended to diverse cell discovery analyses with flexibility to adapt to diverse Infinity Flow experimental designs. pyInfinityFlow is freely available in GitHub (https://github.com/KyleFerchen/pyInfinityFlow) and on PyPI (https://pypi.org/project/pyInfinityFlow/). Package documentation with tutorials on a test dataset is available by Read the Docs (pyinfinityflow.readthedocs.io). The scripts and data for reproducing the results are available at https://github.com/KyleFerchen/pyInfinityFlow/tree/main/analysis_scripts, along with the raw flow cytometry input data.
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影响因子：
48
作者：
Stoeckius M;Hafemeister C;Stephenson W;Houck-Loomis B;Chattopadhyay PK;Swerdlow H;Satija R;Smibert P
通讯作者：
Smibert P
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12.3
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12.3
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Wolf FA;Angerer P;Theis FJ
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30.5
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Triana S;Vonficht D;Jopp-Saile L;Raffel S;Lutz R;Leonce D;Antes M;Hernández-Malmierca P;Ordoñez-Rueda D;Ramasz B;Boch T;Jann JC;Nowak D;Hofmann WK;Müller-Tidow C;Hübschmann D;Alexandrov T;Benes V;Trumpp A;Paulsen M;Velten L;Haas S
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Haas S
影响因子：
32.4
作者：
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Ginhoux, Florent