A knowledge graph framework for automated gating analysis of cytometry data

项目概要/摘要 流式细胞仪和质谱仪提供了多参数单细胞数据，这些数据对于理解细胞周期的变化至关重要。 细胞异质性在各种生物系统。现代多色流式细胞仪 同时常规测量约16个参数。下一代质谱仪 （CyTOF）技术允许同时测量50个或更多参数。甚至 随着细胞计数技术的快速发展， 仍然不够。广泛使用的手动门控分析是知识驱动的，易于操作。 解释，但它是主观的，劳动密集型的，并且不可扩展以处理日益增加的复杂性 的数据。自动数据驱动算法的最新发展能够解决 手动门控的问题，但数据驱动算法的结果往往不直观， 生物专家解读。这些限制造成了流量和质量的关键瓶颈 细胞计数分析。本申请的总体目标是开发一种新颖的框架， 结合知识驱动和数据驱动的方法，实现自动选通 流式细胞术和CyTOF数据的分析。具体目标是：（1）构建知识图谱， 掌握现有的手动门控分析知识，（2）开发自动化门控分析算法， 门控分析，以及（3）使用大规模研究验证知识图框架， ImmPort。拟议的研究是重要的，因为它将使高效和可重复的 门控分析，并提供易于解释的可视化，这两者都是至关重要的 这对研究界很重要。这些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.