High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.
High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.
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DOI：
10.1002/cyto.a.24016
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发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Price KM
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文献类型：
--
作者：
Ferrer-Font L;Mayer JU;Old S;Hermans IF;Irish J;Price KM
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，显著增加了在单细胞水平上可测量的特征数量。因此，新的计算技术，如降维和/或聚类算法是必要的分析，清洁，可视化和解释这些高维数据集。在这个小型比较研究中，我们通过MC或SFC研究了来自同一样本的脾细胞，并使用专家门控、t分布随机邻域嵌入（t-SNE）、均匀流形近似和投影（UMAP）分析和FlowSOM比较了两个高维数据集。当我们将每个数据集下采样到它们的等效细胞数量和参数时，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，仅当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变得明显。总的来说，我们的小型比较研究表明，当手动分析或通过高维聚类或降维算法（如t-SNE，UMAP或FlowSOM）分析时，质谱流式细胞术和光谱流式细胞术均产生相当的结果。然而，需要进行大规模的研究和深入的技术分析，以更详细地评估这些技术之间的差异。
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.
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影响因子：
3.7
作者：
Nguyen, Richard;Perfetto, Stephen;Mahnke, Yolanda D.;Chattopadhyay, Pratip;Roederer, Mario
通讯作者：
Roederer, Mario
影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
16.8
作者：
Bendall SC;Nolan GP;Roederer M;Chattopadhyay PK
通讯作者：
Chattopadhyay PK
影响因子：
17.1
作者：
Horowitz A;Strauss-Albee DM;Leipold M;Kubo J;Nemat-Gorgani N;Dogan OC;Dekker CL;Mackey S;Maecker H;Swan GE;Davis MM;Norman PJ;Guethlein LA;Desai M;Parham P;Blish CA
通讯作者：
Blish CA
DOI：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP