The Statistical and Computational Analysis of Flow Cytometry Data

描述(申请人提供)：流式细胞术是一项数据丰富的技术，在各种人类疾病的基础研究和临床诊断中发挥着关键作用。传统上，大多数细胞学实验都是目视分析的，要么通过一次一维或两维(标记)的连续检查(一种称为“门”的过程，用边界或“门”定义感兴趣的细胞群体)，要么通过汇总统计的非常基本的比较。基于原子质谱仪的细胞学技术的进步将很快允许研究人员查询多达50个标记(而目前的技术约为10个)，使传统的分析方法难以为继。这项新的质量细胞仪技术将产生高通量的高维数据集，为单细胞生物学开辟新的途径。因此，至关重要的是，分析工具和统计方法必须参与这场革命，以充分利用这项技术的潜力。我们正在为流式细胞术和质量细胞术提出新的计算方法和软件工具。这些工具的影响将是为研究人员提供一套工具，这些工具对于从这些数据中提取有意义的信息至关重要。我们将把我们的方法应用到许多不同的场景中，例如识别HIV和疟疾疫苗保护的免疫相关性，识别动态平衡的遗传机制，以及临床预测慢性炎症条件。

DESCRIPTION (provided by applicant): Flow cytometry is a data-rich technology that plays a critical role in basic research and clinical diagnostics for a variety of human diseases. Traditionally, the majority of cytometry experiments have been analyzed visually, either by serial inspection of one or two dimensions (markers) at a time (a process termed "gating", with boundaries or "gates" defining cell populations of interest), or by very basic comparisons of summary statistics. Technological advances in cytometry based on atomic mass spectrometry will soon allow researchers to query up to 50 markers (as opposed to about 10 with current technology), making traditional analysis approaches untenable. This new mass cytometry technology will generate high-throughput high-dimensional datasets, opening up new avenues for single--cell biology. As a consequence, it is essential that analytical tools and statistical methods take part in this revolution to harness the full potential of the technology. We are proposing novel computational methods and software tools for both flow and mass cytometry. The impact of these tools will be to provide researchers with a set of tools that will become essential to extract meaningful information from such data. We will apply our methods to a number of different scenarios such as the identification of immune correlate of protections for HIV and malaria vaccines, the identification of genetic mechanisms of homeostasis, and the clinical prediction of chronic inflammatory conditions.