Division of Systems Biology.
Division of Immunology, Graduate School of Medicine, Nagoya University, Nagoya 4668550, Japan.
Bioinformatics. 2021 Jul 12;37(11):1632-1634. doi: 10.1093/bioinformatics/btaa873.
Recent advancements in high-dimensional single-cell technologies, such as mass cytometry, enable longitudinal experiments to track dynamics of cell populations and identify change points where the proportions vary significantly. However, current research is limited by the lack of tools specialized for analyzing longitudinal mass cytometry data. In order to infer cell population dynamics from such data, we developed a statistical framework named CYBERTRACK2.0. The framework's analytic performance was validated against synthetic and real data, showing that its results are consistent with previous research.
CYBERTRACK2.0 is available at https://github.com/kodaim1115/CYBERTRACK2.
Supplementary data are available at Bioinformatics online.
近年来，高维单细胞技术（如质谱流式细胞术）的发展使得纵向实验能够跟踪细胞群体的动态，并确定比例发生显著变化的转折点。然而，目前的研究受到缺乏专门分析纵向质谱流式细胞术数据的工具的限制。为了从这些数据中推断细胞群体的动态，我们开发了一个名为 CYBERTRACK2.0 的统计框架。该框架的分析性能通过合成数据和真实数据进行了验证，结果与先前的研究一致。
CYBERTRACK2.0 可在 https://github.com/kodaim1115/CYBERTRACK2. 上获得。
补充数据可在 Bioinformatics 在线获得。
