美国宾夕法尼亚大学Robert B. Faryabi研究团队取得一项新突破。他们开发了TooManyCells算法用于识别和可视化单细胞进化枝的关系。相关论文于2020年3月2号发表在《自然-方法学》杂志上。

研究人员开发了TooManyCells，这是一套基于图形的算法，可以高效、无偏倚地识别和可视化细胞的进化枝。TooManyCells引入了一个可视化模型，该模型基于概念与降维方法的正交。TooManyCells还具有一种有效的无矩阵分裂分层谱聚类，它不同于普遍的单分辨率聚类方法。TooManyCells可以对单细胞进化枝进行多分辨率和多维探索。这种方法的优势是可以立即检测出优于大众的聚类和可视化算法的稀有和常见聚类，正如使用现有的单细胞转录组数据集和在白血病T细胞中获得新的耐药性数据建模数据所证明的那样。

据了解，识别和可视化转录相似细胞有助于准确探究由单细胞转录组学所揭示的细胞多样性。但是，广泛使用聚类和可视化算法会产生固定数量的单元簇。固定聚类“分辨率”妨碍了人们识别和可视化细胞状态梯队的能力。

附：英文原文

Title: TooManyCells identifies and visualizes relationships of single-cell clades

Author: Gregory W. Schwartz, Yeqiao Zhou, Jelena Petrovic, Maria Fasolino, Lanwei Xu, Sydney M. Shaffer, Warren S. Pear, Golnaz Vahedi, Robert B. Faryabi

Issue&Volume: 2020-03-02

Abstract: Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of the cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering ‘resolution’ hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a visualization model built on a concept intentionally orthogonal to dimensionality-reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution clustering methods. TooManyCells enables multiresolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms, as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells.

DOI: 10.1038/s41592-020-0748-5

Source: https://www.nature.com/articles/s41592-020-0748-5