Multivariate statistical methods, flow cytometry, and network modeling.

描述(申请人提供)：在生物或医学研究的许多领域，研究人员面临着分析数据集的任务，这些数据集可以被描述为“大样本，中等维度”。一个重要的例子，也是本项目的具体重点，是多参数流式细胞术，其中数据点的数量在数万到数百万之间，并且每个数据点可以提供对多个变量(5到60)的测量。需要新的统计工具来分析和可视化这些数据，并解决相关的假设检验和建模挑战。本研究的目标是为这类多变量数据开发新的统计方法，并在此基础上创建并为多参数流式细胞术提供更有效的数据分析和解释工具。首先，我们将发展一种新的基于简单函数逼近密度的多元密度估计方法。这些估计基本上是基于基本多变量领域的数据自适应分区的直方图。其目的是使用具有很强的理论合理性和良好的经验表现的方法来实现对这些划分的有效学习。我们还将实施并进一步发展这些方法，用于分析多参数流式细胞仪数据。与经典的多色流式细胞术相比，质量细胞术是一种新的细胞学方法，它可以极大地增加每个细胞测量的变量数量。其目的不仅是改进主要的分析任务，如细胞群体识别，而且还为下游分析任务开发新的方法，如正在分析的变量的图形建模。这些方法将通过分发计算机程序和网络服务进行传播。

DESCRIPTION (provided by applicant): In many areas of biological or medical research, investigators are faced with the task of analyzing data sets that can be described as "large sample, moderate dimension". An important example, which is the specific focus of this project, is multi-parameter flow cytometry where the number of data points is in the range of tens of thousands to several millions, and each data point can provide measurements on multiple variables (5 to 60). New statistical tools are needed to analyze and visualize this data, and to address the associated hypothesis testing and modeling challenges. The goal of this research is to develop new statistical methods for this type of multivariate data, and based on these methods, to create and provide more effective data analysis and interpretation tools for multi-parameter flow cytometry. First we will develop a new approach to multivariate density estimation based on the approximation of the density by simple functions. These estimates are essentially histograms based on data adaptive partitions of the basic multivariate domain. The aim is to attain effective learning of these partitions using methods with strong theoretical justification and good empirical performance. We will also implement and further develop these methods for the analysis of multi-parameter flow cytometry data. Particular attention will be paid to mass cytometry which is a new cytometry modality that can greatly increase the number of variables measured per cell, as compared to classical polychromatic flow cytometry. The aim is not only to improve primary analysis tasks such as cell population identification, but also to develop new methods for downstream analysis tasks such as graphical modeling of the variables being analyzed. The methods will be disseminated though distribution of computer programs and also though web service.