A knowledge graph framework for automated gating analysis of cytometry data

项目摘要/摘要 流式细胞术和质量细胞术提供了多参数单细胞数据，这对于理解 不同生物系统中的细胞异质性。现代多色流式细胞仪 常规同时测量约16个参数。新一代质量细胞仪 (CyTOF)技术允许同时测量50个或更多参数。连 随着细胞检测技术的快速发展，分析这种复杂数据的方法 仍然不够充分。广泛使用的手动浇注分析是知识驱动的，并且易于 解释，但它是主观的、劳动密集型的，并且不能扩展以处理日益增长的复杂性 数据的一部分。自动化数据驱动算法的最新发展能够解决 手动选通的问题，但数据驱动算法的结果通常不直观 生物专家来解读。这些限制造成了流量和质量的严重瓶颈 细胞学分析。该应用程序的总体目标是开发一种新的框架，该框架 结合知识驱动和数据驱动的方法，实现自动选通 流式细胞仪和细胞周期图数据分析。具体目标是：(1)构建知识图谱， 获取手动门控分析的现有知识，(2)开发自动化的算法 门限分析，以及(3)通过在 ImmPort。拟议的研究具有重要意义，因为它将使高效和可重复性 选通分析并提供易于解释的可视化，这两个方面都很关键 对研究界来说很重要。这样的贡献将从根本上影响单细胞 分析不同领域的细胞异质性，包括免疫学、传染病、 癌症、艾滋病等等。

Project Summary / Abstract Flow and mass cytometry provide multiparametric single-cell data critical for understanding the cellular heterogeneity in various biological systems. Modern polychromatic flow cytometers simultaneously measure about 16 parameters routinely. The next-generation mass cytometry (CyTOF) technology allows for the simultaneous measurement of 50 or more parameters. Even as the cytometry technology is rapidly advancing, approaches for analyzing such complex data remain inadequate. The widely-used manual gating analysis is knowledge-driven and easy-to- interpret, but it is subjective, labor-intensive, and not scalable to handle the increasing complexity of the data. Recent developments of automated data-driven algorithms are able to address the issues of manual gating, but the results from data-driven algorithms are often not intuitive for biology experts to interpret. These limitations create a critical bottleneck for flow and mass cytometry analysis. The overall objective of this application is to develop a novel framework that combines both knowledge-driven and data-driven approaches to achieve automated gating analysis of flow cytometry and CyTOF data. The specific aims are: (1) build knowledge graphs to capture existing knowledge of manual gating analysis, (2) develop algorithms for automated gating analysis, and (3) validate the knowledge graph framework using large-scale studies in ImmPort. The proposed research is significant because it will enable efficient and reproducible gating analysis and provide visualizations that are easy-to-interpret, both of which are critically important to the research community. Such contributions will fundamentally impact single-cell analysis of cellular heterogeneity in diverse fields including immunology, infectious diseases, cancer, AIDS, among others.