在过去的十年中，高维单细胞技术已经彻底改变了基础和转化免疫学研究，并成为科学家研究免疫系统的工具箱的关键元素。然而，对这些方法产生的数据进行分析通常需要聚类算法和降维表示，这些算法计算强度大、评估和优化困难。在这里，我们提出了细胞测量学聚类优化与评估（Cyclone），这是一个集成降维、聚类、评估和聚类分辨率优化的分析流程，并提供下游可视化工具，便于对各种细胞测量学数据进行分析。我们在各种生物学背景下对Cyclone进行了基准测试和验证，包括质谱细胞测量学（CyTOF）、基于全光谱荧光的细胞测量学和多重免疫荧光（IF）。在每种情况下，Cyclone不仅能够重新得到免疫细胞的黄金标准鉴定结果，还能够无监督地识别与不同生物特征相关的淋巴细胞和单核吞噬细胞亚群。总之，Cyclone流程是一种多功能和易于使用的流程，用于对各种细胞测量学数据集进行聚类的执行、优化和评估，这将进一步推动免疫学研究并为生物发现提供支持。Copyright © 2023 Patel，Jaszczak，Im，Carey，Courau，Bunis，Samad，Avanesyan，Chew，Stenske，Jespersen，Publicover，Edwards，Naser，Rao，Lupin-Jimenez，Krummel，Cooper，Baron，Combes和Fragiadakis.

In the past decade, high-dimensional single-cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation, which are computationally intense and difficult to evaluate and optimize. Here, we present Cytometry Clustering Optimization and Evaluation (Cyclone), an analysis pipeline integrating dimensionality reduction, clustering, evaluation, and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full-spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification but also enables the unsupervised identification of lymphocytes and mononuclear phagocyte subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on a variety of cytometry datasets, which will further power immunology research and provide a scaffold for biological discovery.Copyright © 2023 Patel, Jaszczak, Im, Carey, Courau, Bunis, Samad, Avanesyan, Chew, Stenske, Jespersen, Publicover, Edwards, Naser, Rao, Lupin-Jimenez, Krummel, Cooper, Baron, Combes and Fragiadakis.