Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 197 Ruijin Er Road, Shanghai, 200025, China.
Division of Biomedical Statistics and Informatics, Department of Health Sciences Research and Center for Individualized Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
BMC Bioinformatics. 2021 Mar 22;22(1):138. doi: 10.1186/s12859-021-04054-2.
The rapidly increasing dimensionality and throughput of flow and mass cytometry data necessitate new bioinformatics tools for analysis and interpretation, and the recently emerging single-cell-based algorithms provide a powerful strategy to meet this challenge.
Here, we present CytoTree, an R/Bioconductor package designed to analyze and interpret multidimensional flow and mass cytometry data. CytoTree provides multiple computational functionalities that integrate most of the commonly used techniques in unsupervised clustering and dimensionality reduction and, more importantly, support the construction of a tree-shaped trajectory based on the minimum spanning tree algorithm. A graph-based algorithm is also implemented to estimate the pseudotime and infer intermediate-state cells. We apply CytoTree to several examples of mass cytometry and time-course flow cytometry data on heterogeneity-based cytology and differentiation/reprogramming experiments to illustrate the practical utility achieved in a fast and convenient manner.
CytoTree represents a versatile tool for analyzing multidimensional flow and mass cytometry data and to producing heuristic results for trajectory construction and pseudotime estimation in an integrated workflow.
流式和质谱细胞术数据的维度和通量迅速增加，这需要新的生物信息学工具进行分析和解释，而最近出现的基于单细胞的算法为应对这一挑战提供了强有力的策略。
在这里，我们提出了 CytoTree，这是一个专为分析和解释多维流式和质谱细胞术数据而设计的 R / Bioconductor 包。CytoTree 提供了多种计算功能，集成了无监督聚类和降维中常用的大多数技术，更重要的是，支持基于最小生成树算法构建树状轨迹。还实现了基于图的算法来估计伪时间并推断中间状态细胞。我们将 CytoTree 应用于基于异质性细胞学和分化/重编程实验的质谱细胞术和时程流式细胞术数据的几个示例，以说明以快速方便的方式实现的实际效用。
CytoTree 是一种多功能工具，可用于分析多维流式和质谱细胞术数据，并在集成工作流程中生成轨迹构建和伪时间估计的启发式结果。
