使用持续同源性确定细胞术数据中的临床相关特征
Determining clinically relevant features in cytometry data using persistent homology
摘要：
细胞计数实验产生难以手动解释的高维点云数据。布尔门控技术结合细胞亚群相对丰度的比较是细胞术数据分析的当前标准。然而，这种方法无法捕获隐藏在数据中的更微妙的拓扑特征，特别是如果这些特征被临床数据中的数据变换或显著的批次效应或供体间变化进一步掩盖。对描述COVID-19患者和健康对照中非初始CD 8 + T细胞的公开可用细胞计数数据的分析表明，COVID-19患者和健康对照的单细胞蛋白表达之间存在系统性结构差异。我们通过基于决策树的分类器识别感兴趣的蛋白质，随机采样点并从这些采样点计算持久性图。由此产生的持久性图识别不同密度的细胞计数数据集中的区域，并识别突出的结构，如“肘”。我们计算了健康对照和COVID-19患者随机配对的这些持久性图之间的Wasserstein距离，发现COVID-19患者和健康对照之间在T-bet、Eomes和Ki-67的表达数据中存在系统性结构差异。进一步的分析显示，与健康对照相比，T-bet和Eomes的表达在COVID-19患者的非初始CD 8 + T细胞中显著下调。这一违反直觉的发现可能表明，典型效应CD 8 + T细胞在COVID-19患者中的流行程度低于健康对照。该方法适用于任何细胞术数据集，用于通过拓扑数据分析发现新的见解，这可能难以用标准门控策略或现有的生物信息学工具来确定。
英文摘要：
Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as `elbows'. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.
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