Methods for genomic data with graphical structures

具有图形结构的基因组数据的方法

• 批准号: 7599555
• 负责人: Hongzhe Lee
• 金额: $ 29.07万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7599555
• 关键词: Address; Analysis of Variance; Area; Biological; Biological Process; Charge; Clinical; Collaborations; Complex; Computer software; Cox Proportional Hazards Models; Data; Data Analyses; Data Set; Databases; Development; Disease; Documentation; Event; Failure; Gene Expression; Genes; Genomics; Grant; Graph; Hearing; Heart failure; Human; Internet; Lasso; Linear Models; Machine Learning; Metabolic Pathway; Metadata; Methodology; Methods; Modeling; Multivariate Analysis; Neuroblastoma; Pathway interactions; Pennsylvania; Performance; Phenotype; Procedures; Proteomics; Regulatory Pathway; Research Personnel; Sampling; Signal Pathway; Statistical Methods; Statistical Models; Structure; System; Testing; Time; Universities; Work; biological systems; clinical phenotype; computer program; computerized tools; genetic analysis; heart allograft; high throughput technology; human disease; insight; interest; novel; programs; research study; response; software development; theories; vector

DESCRIPTION (provided by applicant): The broad, long-term objective of this project concerns the development of novel statistical methods and computational tools for statistical and probabilistic modeling of genomic data motivated by important biological questions and experiments. The specific aim of the current project is to develop new statistical models and methods for analysis of genomic data with graphical structures, focusing on methods for analyzing genetic pathways and networks, including the development of nonparametric pathway-smooth tests for two-sample and analysis of variance problems for identifying pathways with perturbed activity between two or multiple experimental conditions, the development of group Lasso and group threshold gradient descent regularized estimation procedures for the pathway-smoothed generalized linear models, Cox proportional hazards models and the accelerated failure time models in order to identify pathways that are related to various clinical phenotypes. These methods hinge on novel integration of spectral graph theory, non-parametric methods for analysis of multivariate data and regularized estimation methods fro statistical learning. The new methods can be applied to different types of genomic data and will ideally facilitate the identification of genes and biological pathways underlying various complex human diseases and complex biological processes. The project will also investigate the robustness, power and efficiencies o these methods and compare them with existing methods. In addition, this project will develop practical a feasible computer programs in order to implement the proposed methods, to evaluate the performance o these methods through application to real data on microarray gene expression studies of human hear failure, cardiac allograft rejection and neuroblastoma. The work proposed here will contribute both statistical methodology to modeling genomic data with graphical structures, to studying complex phenotypes and biological systems and methods for high-dimensional data analysis, and offer insight into each of the clinical areas represented by the various data sets to evaluate these new methods. All programs developed under this grant and detailed documentation will be made available free-of-charge to interested researchers via the World Wide Web.

描述（由申请人提供）：该项目的长期目标是开发新的统计方法和计算工具，用于重要的生物学问题和实验驱动的基因组数据的统计和概率建模。当前项目的具体目标是开发新的统计模型和方法，用于分析具有图形结构的基因组数据，重点是分析遗传途径和遗传网络的方法，包括开发用于两样本的非参数路径平滑测试和方差分析问题，以确定在两个或多个实验条件之间具有扰动活动的途径。为路径平滑的广义线性模型、Cox比例风险模型和加速失效时间模型开发了组Lasso和组阈值梯度下降正则化估计程序，以识别与各种临床表型相关的通路。这些方法依赖于谱图理论的新颖整合，多变量数据分析的非参数方法和统计学习的正则化估计方法。新方法可应用于不同类型的基因组数据，并将理想地促进识别各种复杂人类疾病和复杂生物过程背后的基因和生物途径。该项目还将调查这些方法的鲁棒性、功率和效率，并将它们与现有方法进行比较。此外，本项目将开发一个实际可行的计算机程序来实现所提出的方法，通过应用于人类听力衰竭、心脏异体移植排斥和神经母细胞瘤的微阵列基因表达研究的实际数据来评估这些方法的性能。这里提出的工作将有助于用图形结构建模基因组数据的统计方法，研究复杂表型和生物系统以及高维数据分析的方法，并提供对各种数据集所代表的每个临床领域的见解，以评估这些新方法。在此资助下开发的所有项目和详细文件将通过万维网免费提供给感兴趣的研究人员。