High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.,Cytometry Part A

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High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.
Cytometry Part A ( IF 2.5 ) Pub Date : 2020-04-15 , DOI: 10.1002/cyto.a.24016
Laura Ferrer-Font _{1,

2} , Johannes U Mayer ₁ , Samuel Old ₁ , Ian F Hermans _{1,

2} , Jonathan Irish _{3,

4} , Kylie M Price ₁

Affiliation

The arrival of mass cytometry (MC) and, more recently, spectral flow cytometry (SFC) has revolutionized the study of cellular, functional and phenotypic diversity, significantly increasing the number of characteristics measurable at the single‐cell level. As a consequence, new computational techniques such as dimensionality reduction and/or clustering algorithms are necessary to analyze, clean, visualize, and interpret these high‐dimensional data sets. In this small comparison study, we investigated splenocytes from the same sample by either MC or SFC and compared both high‐dimensional data sets using expert gating, t ‐distributed stochastic neighbor embedding (t‐SNE), uniform manifold approximation and projection (UMAP) analysis and FlowSOM. When we downsampled each data set to their equivalent cell numbers and parameters, our analysis yielded highly comparable results. Differences between the data sets only became apparent when the maximum number of parameters in each data set were assessed, due to differences in the number of recorded events or the maximum number of assessed parameters. Overall, our small comparison study suggests that mass cytometry and spectral flow cytometry both yield comparable results when analyzed manually or by high‐dimensional clustering or dimensionality reduction algorithms such as t‐SNE, UMAP, or FlowSOM. However, large scale studies combined with an in‐depth technical analysis will be needed to assess differences between these technologies in more detail. © 2020 International Society for Advancement of Cytometry

中文翻译：

高维数据分析算法可为质谱流式细胞术和光谱流式细胞术数据产生可比的结果。

质谱流式细胞术 (MC) 和最近的光谱流式细胞术 (SFC) 的出现彻底改变了细胞、功能和表型多样性的研究，显着增加了单细胞水平上可测量的特征数量。因此，需要新的计算技术（例如降维和/或聚类算法）来分析、清理、可视化和解释这些高维数据集。在这项小型比较研究中，我们通过 MC 或 SFC 研究了同一样本的脾细胞，并使用专家门控、 t分布随机邻域嵌入 (t-SNE)、均匀流形逼近和投影 (UMAP) 比较了两个高维数据集分析和 FlowSOM。当我们将每个数据集下采样为其等效的细胞数量和参数时，我们的分析产生了高度可比的结果。由于记录的事件数量或评估参数的最大数量存在差异，只有在评估每个数据集中的最大参数数量时，数据集之间的差异才会变得明显。总体而言，我们的小型比较研究表明，在手动分析或通过高维聚类或降维算法（例如 t-SNE、UMAP 或 FlowSOM）分析时，质谱流式细胞术和光谱流式细胞术都可以产生可比较的结果。然而，需要大规模研究与深入的技术分析相结合，以更详细地评估这些技术之间的差异。 © 2020 国际细胞计数促进会

更新日期：2020-04-15

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