Uncovering axes of variation among single-cell cancer specimens
Uncovering axes of variation among single-cell cancer specimens
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DOI：
10.1038/s41592-019-0689-z
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发表时间：
2020-01-13
期刊：
影响因子：
48
通讯作者：
Krishnaswamy, Smita
中科院分区：
文献类型：
--
作者：
Chen, William S.;Zivanovic, Nevena;Krishnaswamy, Smita
虽然已经开发了几种工具来绘制单个细胞之间的变异轴，但没有类似的方法来识别单细胞分辨率的多细胞生物标本之间的变异轴。为此，我们开发了“表型移土距离”（PhEMD）。PhEMD是一种嵌入“流形的流形”的通用方法，其中（生物标本的）高级流形中的每个数据点表示跨越（细胞的）低级流形的点的集合。我们将PhEMD应用于新生成的药物筛选数据集，并证明PhEMD揭示了大量扰动条件下细胞亚群变异的轴。此外，我们表明PhEMD可以用来推断生物标本的表型，而不是直接分析。应用于临床数据集，PhEMD生成患者状态空间的地图，突出显示患者之间差异的来源。PhEMD是可扩展的，兼容领先的批量效应校正技术，并可推广到多个实验设计。表型地球移动距离（PhEMD）促进了单细胞实验条件的比较，每个条件都是一个高维数据集，并确定了多细胞生物标本之间的变异轴。
While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed 'phenotypic earth mover's distance' (PhEMD). PhEMD is a general method for embedding a 'manifold of manifolds', in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.Phenotypic earth mover's distance (PhEMD) facilitates the comparison of single-cell experimental conditions, each of which is a high-dimensional dataset, and identifies axes of variation among multicellular biospecimens.