Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting
Reverse-engineering flow-cytometry gating strategies for phenotypic labelling and high-performance cell sorting
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DOI：
10.1093/bioinformatics/bty491
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发表时间：
2019-01-15
期刊：
影响因子：
5.8
通讯作者：
Newell, Evan W.
中科院分区：
文献类型：
--
作者：
Becht, Etienne;Simoni, Yannick;Newell, Evan W.
动机：最近的流式细胞仪和质谱仪生成20至40维和100万个单细胞的数据集。由此，许多工具促进了与疾病或生理学相关的新细胞群的发现。这些新的细胞群体需要识别新的门控策略，但是当维数增加时，门控策略变得指数地更难以优化。为了促进这一步，我们开发了Hypergate，这是一种算法，它给出了一个感兴趣的细胞群体，确定了一个优化的门控策略，以获得高产量和纯度。结果：Hypergate在公共数据集上实现了比人类专家，支持向量机和随机森林更高的产量和纯度。我们用它来重新审视一些既定的门控策略，用于识别先天性淋巴样细胞，识别简洁有效的策略，允许门控这些细胞与更少的参数，但更高的产量和纯度比目前的标准。对于表型描述，Hypergate的输出与领域的知识一致，并且比竞争方法的输出更稀疏。
Motivation: Recent flow and mass cytometers generate datasets of dimensions 20 to 40 and a million single cells. From these, many tools facilitate the discovery of new cell populations associated with diseases or physiology. These new cell populations require the identification of new gating strategies, but gating strategies become exponentially more difficult to optimize when dimensionality increases. To facilitate this step, we developed Hypergate, an algorithm which given a cell population of interest identifies a gating strategy optimized for high yield and purity.Results: Hypergate achieves higher yield and purity than human experts, Support Vector Machines and Random-Forests on public datasets. We use it to revisit some established gating strategies for the identification of innate lymphoid cells, which identifies concise and efficient strategies that allow gating these cells with fewer parameters but higher yield and purity than the current standards. For phenotypic description, Hypergate's outputs are consistent with fields' knowledge and sparser than those from a competing method.