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Analyzing high-dimensional cytometry data using FlowSOM
Nature Protocols ( IF 14.8 ) Pub Date : 2021-06-25 , DOI: 10.1038/s41596-021-00550-0
Katrien Quintelier 1, 2, 3 , Artuur Couckuyt 1, 2 , Annelies Emmaneel 1, 2 , Joachim Aerts 3 , Yvan Saeys 1, 2 , Sofie Van Gassen 1, 2
Affiliation  

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.



中文翻译:

使用 FlowSOM 分析高维细胞计数数据

细胞计数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变得不足。因此,该领域正慢慢朝着更自动化的方法发展,在本文中,我们描述了使用 FlowSOM 分析高维细胞计数数据的协议,FlowSOM 是一种基于自组织图的聚类和可视化算法。FlowSOM 用于以无人监督的方式将细胞群与细胞计数数据区分开来,有助于在免疫学和肿瘤学等领域获得更深入的见解。自最初的 FlowSOM 发布(2015 年)以来,我们已经在各种数据集上验证了该工具,为了编写此协议,我们利用这种经验来提高包的用户友好性(例如,全面的功能取代了通常需要的脚本)。在原始文件主要侧重于算法描述的情况下,该协议为用户提供了有关如何实施该过程、详细参数描述和故障排除建议的指南。该协议提供了清晰注释的 R 代码,因此与所有对计算高维分析感兴趣而不需要强大的生物信息学背景的科学家相关。我们展示了完整的工作流程,从数据准备(例如补偿、转换和质量控制)开始,包括对不同 FlowSOM 参数和可视化选项的详细讨论,最后总结如何将结果进一步用于回答生物学问题,例如兴趣组之间的统计比较。

更新日期:2021-06-25
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