Multivariate statistical methods, flow cytometry, and network modeling.

多元统计方法、流式细胞术和网络建模。

基本信息

批准号：
8891463
负责人：
Wing H. WONG
金额：
$ 29.73万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-07-15 至 2018-04-30
项目状态：
已结题

项目摘要

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.

描述（由申请人提供）：在生物学或医学研究的许多领域中，研究人员面临分析可以描述为“大型样本，中等维度”的数据集的任务。一个重要的例子是该项目的特定重点，是多参数流式细胞仪，其中数据点的数量在数万到数百万的范围内，每个数据点可以在多个变量（5至60）上提供测量值（5至60）。需要新的统计工具来分析和可视化这些数据，并解决相关的假设测试和建模挑战。这项研究的目的是为此类多元数据开发新的统计方法，并基于这些方法，为多参数流式细胞仪创建并提供更有效的数据分析和解释工具。首先，我们将根据简单函数的密度近似来开发一种新的多元密度估计方法。这些估计基本上是基于基本多元域的数据自适应分区的直方图。目的是使用具有强大理论理由和良好经验绩效的方法来有效学习这些分区。我们还将实施并进一步开发这些方法，以分析多参数流式细胞仪数据。与经典的多色流式细胞仪相比，将特别关注质量细胞仪，这是一种新的细胞仪方式，可以大大增加每个细胞测量的变量的数量。其目的不仅是为了改善主要分析任务，例如细胞群体识别，还旨在为下游分析任务（例如被分析的变量的图形建模）开发新方法。这些方法将通过计算机程序的分布以及Web服务传播。