High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we develop cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We use cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high-dimensional data from preprocessing to biological interpretation.

高维细胞术 （HDC） 是一种用于研究复杂生物系统中单细胞表型的强大技术。尽管近年来技术发展和可负担性使 HDC 广泛可用，但技术进步并没有与能够充分利用生成的复杂数据的分析方法的充分发展相结合。虽然已经有几种分析平台和生物信息学工具可用于分析 HDC 数据，但这些要么是 Web 托管的，可扩展性有限，要么是为专业计算生物学家设计的，这使得湿实验室科学家无法使用它们。此外，由于缺少统一的分析生态系统，端到端 HDC 数据分析进一步受到阻碍，需要研究人员浏览多个平台和软件包才能完成分析。为了弥合 HDC 中的这一数据分析差距，我们开发了 cyCONDOR，这是一个易于使用的计算框架，不仅涵盖了细胞术数据分析的所有基本步骤，还包括一系列下游功能和工具，以扩展数据的生物学解释。cyCONDOR 的全面功能套件，包括引导式预处理、聚类、降维和机器学习算法，有助于将 cyCONDOR 无缝集成到临床相关环境中，其中可扩展性和疾病分类对于 HDC 在临床实践中的广泛采用至关重要。此外，cyCONDOR 的高级分析功能（例如伪时间分析和批量集成）为研究人员提供了从数据中提取更深入见解的工具。 我们对来自不同组织和技术的各种数据使用 cyCONDOR，展示了其多功能性，以协助分析从预处理到生物学解释的高维数据。