Multivariate statistical methods, flow cytometry, and network modeling.

多元统计方法、流式细胞术和网络建模。

• 批准号: 8664732
• 负责人: Wing H. WONG
• 金额: $ 29.78万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/8664732
• 关键词: Address; Area; Attention; Biology; Cells; Computer software; Computing Methodologies; Cytometry; Data; Data Analyses; Data Set; Databases; Dimensions; Flow Cytometry; Goals; Learning; Measurement; Measures; Medical Research; Medicine; Methods; Modality; Modeling; Performance; Population; Research; Research Personnel; Sampling; Statistical Methods; Testing; analytical method; base; biological research; computer program; density; design; improved; network models; novel; novel strategies; public health relevance; task analysis; tool; web services

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，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，这是该项目的具体重点，是多参数流式细胞术，其中数据点的数量在数万到数百万的范围内，每个数据点可以提供多个变量（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，并基于这些方法，为多参数流式细胞术创建和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方法，多元密度估计的基础上近似的密度由简单的功能。这些估计基本上是基于基本多变量域的数据自适应分区的直方图。其目的是实现有效的学习这些分区使用方法具有较强的理论依据和良好的经验表现。我们还将实施并进一步开发这些方法，用于分析多参数流式细胞术数据。将特别注意质量细胞计数，这是一种新的细胞计数模式，可以大大增加每个细胞测量的变量的数量相比，经典的多色流式细胞术。其目的不仅是改善主要分析任务，如细胞群体识别，而且还为下游分析任务开发新方法，如被分析变量的图形建模。这些方法将通过分发计算机程序和网络服务传播。