Biomedical Informatics Training Program, Stanford University Medical School, Stanford, CA 94305;Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, and.
Institute of Molecular Life Sciences, University of Zurich, CH-8057 Zurich, Switzerland.
Proc Natl Acad Sci U S A. 2014 Jul 1;111(26):E2770-7. doi: 10.1073/pnas.1408792111. Epub 2014 Jun 16.
Elucidation and examination of cellular subpopulations that display condition-specific behavior can play a critical contributory role in understanding disease mechanism, as well as provide a focal point for development of diagnostic criteria linking such a mechanism to clinical prognosis. Despite recent advancements in single-cell measurement technologies, the identification of relevant cell subsets through manual efforts remains standard practice. As new technologies such as mass cytometry increase the parameterization of single-cell measurements, the scalability and subjectivity inherent in manual analyses slows both analysis and progress. We therefore developed Citrus (cluster identification, characterization, and regression), a data-driven approach for the identification of stratifying subpopulations in multidimensional cytometry datasets. The methodology of Citrus is demonstrated through the identification of known and unexpected pathway responses in a dataset of stimulated peripheral blood mononuclear cells measured by mass cytometry. Additionally, the performance of Citrus is compared with that of existing methods through the analysis of several publicly available datasets. As the complexity of flow cytometry datasets continues to increase, methods such as Citrus will be needed to aid investigators in the performance of unbiased--and potentially more thorough--correlation-based mining and inspection of cell subsets nested within high-dimensional datasets.
阐明和检查表现出特定条件行为的细胞亚群，可以在理解疾病机制方面发挥关键作用，并为将这种机制与临床预后联系起来的诊断标准的制定提供重点。尽管单细胞测量技术最近取得了进展，但通过手动努力识别相关细胞亚群仍然是标准做法。随着像质谱流式细胞术这样的新技术增加了单细胞测量的参数化，手动分析固有的可扩展性和主观性会减缓分析和进展速度。因此，我们开发了 Citrus（聚类识别、特征描述和回归），这是一种用于识别多维细胞测定数据集分层亚群的基于数据的方法。通过质谱流式细胞术测量的刺激外周血单核细胞数据集，证明了 Citrus 的方法。此外，通过分析几个公开可用的数据集，比较了 Citrus 的性能与现有方法的性能。随着流式细胞术数据集的复杂性不断增加，像 Citrus 这样的方法将需要帮助研究人员在高维数据集中对嵌套的细胞亚群进行基于相关性的无偏挖掘和检查。
