Department of Infection and Immunity, Luxembourg Institute of Health, L-4354 Esch-sur-Alzette, Luxembourg.
Bioinformatics & AI, Department of Medical Informatics, Luxembourg Institute of Health, L-1445 Strassen, Luxembourg.
Bioinformatics. 2024 Oct 1;40(10). doi: 10.1093/bioinformatics/btae595.
Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters.
We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow.
CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.
通过细胞仪进行单细胞分析已成为生物学、免疫学和临床转化医学的关键技术。正确注释是指根据标志物表达识别聚类为特定细胞群体，这是分析的关键步骤。其准确性决定了对生物数据的正确解释。尽管各种聚类算法取得了进展，但聚类数据的注释仍然是一项手动、耗时且容易出错的任务。我们开发了一个用户友好的聚类注释和差异丰度检测工具，可以应用于自组织映射聚类算法生成的数据，从而简化了由数百或数千个聚类组成的数据集的注释过程。
我们提出了 CyCadas，这是一种半自动软件工具，通过提供可视化和计算指导，简化了细胞仪数据中的聚类注释。CyCadas 满足了高效准确注释高分辨率聚类细胞仪数据的关键需求，与手动门控方法和手动注释聚类数据相比，大大减少了分析所需的时间。该工具具有用户友好的界面、用于数据探索的可视化工具以及自动阈值估计，以分离负和正标记表达。它可以在基于树的数据结构中定义和注释多个聚类中的细胞表型。最后，它可以计算各种细胞群体在不同条件下的丰度，并进行统计解释。它是简化细胞仪工作流程的研究人员的理想资源。
CyCadas 可在以下网址获得开源版本：https://github.com/DII-LIH-Luxembourg/cycadas。
