美国耶鲁大学医学院Smita Krishnaswamy与瑞士苏黎世大学Bernd Bodenmiller等研究人员合作实现了对单细胞癌标本中变化轴的绘制。相关论文2020年1月13日在线发表在《自然—方法学》上。

研究人员表示，尽管目前已经开发了几种工具来绘制单个细胞之间的变化轴，但还没有类似的方法可以识别以单细胞分辨率分析的多细胞生物样本之间的变异轴。

为此，研究人员开发了PhEMD（phenotypic earth mover’s distance）。PhEMD是一种嵌入“manifold of manifolds”的通用方法，其中（生物样本的）较高水平的流形中的每个数据点代表跨越（细胞）较低水平的流形的点的集合。研究人员将PhEMD应用于新生成的药物筛选数据集，并证明PhEMD揭示了一大组扰动条件之间的细胞亚群变化轴。此外，研究人员表明PhEMD可用于推断未直接分析的生物标本的表型。将PhEMD应用于临床数据集，可生成患者状态空间图，从而突出显示患者之间差异的来源。PhEMD具有可扩展性，可以与领先的批量效应校正技术兼容，并且可以推广到多个实验设计中。

附：英文原文

Title: Uncovering axes of variation among single-cell cancer specimens

Author: William S. Chen, Nevena Zivanovic, David van Dijk, Guy Wolf, Bernd Bodenmiller, Smita Krishnaswamy

Issue&Volume: 2020-01-13

Abstract: 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.

DOI: 10.1038/s41592-019-0689-z

Source: https://www.nature.com/articles/s41592-019-0689-z