Characterizing cell subsets using marker enrichment modeling.
Characterizing cell subsets using marker enrichment modeling.
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DOI：
10.1038/nmeth.4149
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发表时间：
2017-03
期刊：
影响因子：
48
通讯作者：
Irish JM
中科院分区：
文献类型：
--
作者：
Diggins KE;Greenplate AR;Leelatian N;Wogsland CE;Irish JM
目前，从单细胞数据中学习细胞身份依赖于人类专家。在这里，我们提出了标记丰富模型(MEM)，这是一种通过量化上下文特征丰富并报告人类和机器可读的文本标签来客观描述单元的算法。Mem在从荧光和质量细胞术描述免疫和癌细胞亚群方面优于传统指标。MEM提供了一种定量语言来交流在复杂组织中观察到的新的和已建立的细胞类型的特征。
Learning cell identity from single-cell data presently relies on human experts. Here, we present Marker Enrichment Modeling (MEM), an algorithm that objectively describes cells by quantifying contextual feature enrichment and reporting a human and machine-readable text label. MEM outperformed traditional metrics in describing immune and cancer cell subsets from fluorescence and mass cytometry. MEM provides a quantitative language to communicate characteristics of new and established cytotypes observed in complex tissues.