Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
Data Mining and Modeling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium.
Nat Protoc. 2021 Aug;16(8):3775-3801. doi: 10.1038/s41596-021-00550-0. Epub 2021 Jun 25.
The dimensionality of cytometry data has strongly increased in the last decade, and in many situations the traditional manual downstream analysis becomes insufficient. The field is therefore slowly moving toward more automated approaches, and in this paper we describe the protocol for analyzing high-dimensional cytometry data using FlowSOM, a clustering and visualization algorithm based on a self-organizing map. FlowSOM is used to distinguish cell populations from cytometry data in an unsupervised way and can help to gain deeper insights in fields such as immunology and oncology. Since the original FlowSOM publication (2015), we have validated the tool on a wide variety of datasets, and to write this protocol, we made use of this experience to improve the user-friendliness of the package (e.g., comprehensive functions replacing commonly required scripts). Where the original paper focused mainly on the algorithm description, this protocol offers user guidelines on how to implement the procedure, detailed parameter descriptions and troubleshooting recommendations. The protocol provides clearly annotated R code, and is therefore relevant for all scientists interested in computational high-dimensional analyses without requiring a strong bioinformatics background. We demonstrate the complete workflow, starting from data preparation (such as compensation, transformation and quality control), including detailed discussion of the different FlowSOM parameters and visualization options, and concluding with how the results can be further used to answer biological questions, such as statistical comparison between groups of interest. An average FlowSOM analysis takes 1-3 h to complete, though quality issues can increase this time considerably.
流式细胞术数据的维度在过去十年中大幅增加，在许多情况下，传统的手动下游分析已经不够用。因此，该领域正慢慢转向更自动化的方法，本文介绍了使用 FlowSOM 分析高维流式细胞术数据的方案，FlowSOM 是一种基于自组织映射的聚类和可视化算法。FlowSOM 用于在无监督的情况下区分流式细胞术数据中的细胞群体，并有助于深入了解免疫学和肿瘤学等领域。自 2015 年原始 FlowSOM 论文发表以来，我们已经在各种数据集上验证了该工具，为了编写本方案，我们利用了这些经验来提高该软件包的用户友好性（例如，常用脚本的综合功能）。原始论文主要侧重于算法描述，而本方案则为用户提供了如何实施该程序的指南、详细的参数描述和故障排除建议。本方案提供了带有注释的 R 代码，因此适用于所有对计算高维分析感兴趣的科学家，而无需强大的生物信息学背景。我们演示了从数据准备（例如补偿、转换和质量控制）开始的完整工作流程，包括对不同 FlowSOM 参数和可视化选项的详细讨论，并以如何进一步利用结果来回答生物学问题（例如，对感兴趣的组进行统计比较）结束。平均而言，FlowSOM 分析需要 1-3 小时才能完成，但质量问题可能会大大增加此时间。
