Multi-Way Semilinear Methods with Applications to Microarray Data

多路半线性方法在微阵列数据中的应用

• 批准号: 0604571
• 负责人: Cun-Hui Zhang
• 金额: $ 13.96万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0604571&HistoricalAwards=false
• 关键词: Multi; Way; Semilinear; Methods; Applications

This research focuses on developing statistical models, methods, and theory for normalization and significance analysis of microarray data and similar applications. Microarray technology has become an important tool for quantitatively monitoring gene expression patterns and has been widely used in functional genomics. The investigator and his collaborators have proposed and developed a two-way semilinear model for normalization and analysis of cDNA microarray data. The purpose of this research is to extend and further develop this methodology and investigate its theoretical justifications. The project covers a wide range of specific problems including multi-way semilinear models, normalization and analysis of high-density oligonucleotide arrays, incorporation of control/spike genes and biological pathway information, location-scale normalization, estimation of noise level, incorporation of data quality measurements, invertibility of information operator, asymptotic equivalence to ideal/oracle estimators of gene effects, and more.The multi-way semilinear model is an extension of widely used analysis of variance and semilinear regression models. Its applications to microarray experiments present challenging methodological and theoretical problems with high-dimensional complex datasets. These datasets have the following features: large number of unknown parameters for gene effects and small number of samples, many nonparametric components, co-linearity between bases for the estimations of nonparametric and parametric components, stochastically dependent covariates with spatially inhomogeneous distributions, interactions between observed gene expressions and possibly unobservable gene clusters and biological pathways, inhomogeneous noise level, and more. This research is motivated and will be directly applicable to functional genomic studies using microarray and similar technologies. It will also be directly applicable to high-throughput screening of chemical compounds in drug discovery experiments. This research will have significant educational impact.

该研究的重点是开发用于微阵列数据和类似应用的标准化和显著性分析的统计模型、方法和理论。基因芯片技术已成为定量监测基因表达模式的重要工具，并在功能基因组学研究中得到广泛应用。研究人员和他的合作者提出并开发了一个双向半线性模型，用于cDNA微阵列数据的归一化和分析。本研究的目的是扩展和进一步发展这一方法，并探讨其理论依据。该项目涵盖了广泛的具体问题，包括多路半线性模型，高密度寡核苷酸阵列的归一化和分析，控制/尖峰基因和生物途径信息的整合，位置尺度归一化，噪声水平估计，数据质量测量的整合，信息算子的可逆性，基因效应的理想/oracle估计的渐近等价性，多向半线性模型是广泛使用的方差分析和半线性回归模型的扩展。它的应用微阵列实验提出了具有挑战性的方法和理论问题，高维复杂的数据集。这些数据集具有以下特征：大量未知的基因效应参数和少量样本，许多非参数成分，非参数和参数成分估计的基础之间的共线性，随机依赖的协变量与空间非均匀分布，观察到的基因表达和可能不可观察的基因簇和生物途径之间的相互作用，非均匀噪声水平，等等。这项研究的动机，并将直接适用于功能基因组研究使用微阵列和类似的技术。它也将直接适用于药物发现实验中化合物的高通量筛选。这项研究将产生重大的教育影响。