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Making the most of high-dimensional cytometry data
Immunology and Cell Biology ( IF 4 ) Pub Date : 2021-04-02 , DOI: 10.1111/imcb.12456
Felix MD Marsh‐Wakefield 1, 2, 3 , Andrew J Mitchell 4 , Samuel E Norton 5, 6 , Thomas Myles Ashhurst 2, 7, 8 , Julia KH Leman 6 , Joanna M Roberts 9 , Jessica E Harte 6 , Helen M McGuire 2, 8, 10 , Roslyn A Kemp 6
Affiliation  

High-dimensional cytometry represents an exciting new era of immunology research, enabling the discovery of new cells and prediction of patient responses to therapy. A plethora of analysis and visualization tools and programs are now available for both new and experienced users; however, the transition from low- to high-dimensional cytometry requires a change in the way users think about experimental design and data analysis. Data from high-dimensional cytometry experiments are often underutilized, because of both the size of the data and the number of possible combinations of markers, as well as to a lack of understanding of the processes required to generate meaningful data. In this article, we explain the concepts behind designing high-dimensional cytometry experiments and provide considerations for new and experienced users to design and carry out high-dimensional experiments to maximize quality data collection.

中文翻译:

充分利用高维细胞计数数据

高维细胞计数代表了免疫学研究的一个激动人心的新时代,能够发现新细胞并预测患者对治疗的反应。大量的分析和可视化工具和程序现在可供新老用户使用;然而,从低维细胞术向高维细胞术的转变需要用户对实验设计和数据分析的思考方式发生变化。由于数据的大小和标记可能组合的数量,以及缺乏对生成有意义数据所需的过程的了解,来自高维细胞计数实验的数据通常未被充分利用。在本文中,
更新日期:2021-04-02
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