Wiggins Laura, Lacy Stuart, Park Graeme, Marrison Joanne, Powell Ben, Cimini Beth, O'Toole Peter, Wilson Julie, Brackenbury William J
Department of Materials Science and Engineering, University of Sheffield, Sheffield, UK.
Wolfson Atmospheric Chemistry Laboratories, University of York, York, UK.
J Microsc. 2025 Apr 24. doi: 10.1111/jmi.13416.
We previously developed the CellPhe toolkit, an open-source R package for automated cell phenotyping from ptychography time-lapse videos. To align with the growing adoption of python-based image analysis tools and to enhance interoperability with widely used software for cell segmentation and tracking, we developed a python implementation of CellPhe, named CellPhePy. CellPhePy preserves all of the core functionality of the original toolkit, including single-cell phenotypic feature extraction, time-series analysis, feature selection and cell type classification. In addition, CellPhePy introduces significant enhancements, such as an improved method for identifying features that differentiate cell populations and extended support for multiclass classification, broadening its analytical capabilities. Notably, the CellPhePy package supports CellPose segmentation and TrackMate tracking, meaning that a set of microscopy images are the only required input with segmentation, tracking and feature extraction fully automated for downstream analysis, without reliance on external applications. The workflow's increased flexibility and modularity make it adaptable to different imaging modalities and fully customisable to address specific research questions. CellPhePy can be installed via PyPi or GitHub, and we also provide a CellPhePy GUI to aid user accessibility.
我们之前开发了CellPhe工具包，这是一个用于从叠层成像延时视频中进行自动细胞表型分析的开源R包。为了与越来越多采用的基于Python的图像分析工具保持一致，并增强与广泛使用的细胞分割和跟踪软件的互操作性，我们开发了CellPhe的Python实现版本，名为CellPhePy。CellPhePy保留了原始工具包的所有核心功能，包括单细胞表型特征提取、时间序列分析、特征选择和细胞类型分类。此外，CellPhePy还引入了重大改进，例如一种改进的方法，用于识别区分细胞群体的特征，并扩展了对多类分类的支持，拓宽了其分析能力。值得注意的是，CellPhePy包支持CellPose分割和TrackMate跟踪，这意味着一组显微镜图像是唯一需要的输入，分割、跟踪和特征提取完全自动化，可用于下游分析，而无需依赖外部应用程序。该工作流程增加的灵活性和模块化使其能够适应不同的成像模式，并完全可定制以解决特定的研究问题。CellPhePy可以通过PyPi或GitHub进行安装，我们还提供了一个CellPhePy GUI以方便用户使用。
