Statistical Methods for Ultrahigh-dimensional Biomedical Data

超高维生物医学数据的统计方法

• 批准号: 8423354
• 负责人: Jianqing Fan
• 金额: $ 25.43万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8423354
• 关键词: Address; Bioinformatics; Carcinogens; Classification; Clinical; Cluster Analysis; Computer software; Cox Proportional Hazards Models; Data; Data Set; Development; Gene Expression; Gene Expression Profiling; Gene Proteins; Genes; Harvest; Health; Human; Inherited; Investigation; Linear Models; Liver; Lung Neoplasms; Malignant Neoplasms; Methods; Migration Inhibitory Factor; Modeling; Molecular; Multiple Myeloma; Necrosis; Neuroblastoma; Outcome; Pharmaceutical Preparations; Proteins; Proteomics; Quality Control; Regression Analysis; Research; Sampling; Statistical Methods; Survival Analysis; Techniques; Theoretical Studies; Therapeutic; Time; Toxicogenomics; anticancer research; gene interaction; high throughput analysis; innovative technologies; malignant breast neoplasm; member; neuroblastoma cell; novel; novel strategies; phenylpyruvate tautomerase; protein expression; screening; simulation; theories; therapeutic target; tool

DESCRIPTION (provided by applicant): This proposal develops novel statistical methods to select a small group of molecules from high-throughput data such as microarray and proteomic data from cancer research. The challenge of the study is the ultrahigh dimensionality inherited in these studies, particular when gene-gene interactions are introduced. The ultrahigh dimensionality has large impact on statistical computation, methodological developments, and theoretical studies. The challenge will be dealt by using the proposed novel independence screening methods, which also addresses the computational demand and stability, and the issues of stochastic error accumulation in ultra-high dimensional statistical inferences. An iterative independence screening method is introduced to find hidden signature genes that are marginally unimportant but jointly extremely important to the clinical outcomes. It also enables us to eliminate redundant molecules that are marginally highly but jointly weakly associated with clinical outcomes. With number of features surely reduced to a manageable level, penalized pseudo-likelihood methods will be introduced to further select relevant genes. In addition, methods for finding synergetic groups of molecules are introduced. The idea of independence screening and its iterated version will be applied to various statistical problems from the analysis of high throughput data, ranging from ultrahigh dimensional regression and classification to the analysis of survival time, estimation of genewide variance, and normalization of microarrays. The efficacy of the proposed methods will be evaluated via asymptotic theory and simulation studies. Data sets from on-going biomedical studies on cancer such as breast cancer, multiple myeloma, neuroblastoma, lung tumor, and liver carcigogen will be critically analyzed using the newly developed statistical and bioinformatic tools.

描述（由申请人提供）：该提案开发了新颖的统​​计方法，从高通量数据（例如来自癌症研究的微阵列和蛋白质组数据）中选择一小群分子。这项研究的挑战是这些研究中继承的超高维度，特别是当引入基因与基因相互作用时。超高维对统计计算、方法发展和理论研究具有巨大影响。这一挑战将通过使用所提出的新颖的独立筛选方法来解决，该方法还解决了计算需求和稳定性以及超高维统计推断中的随机误差累积问题。引入迭代独立筛选方法来寻找隐藏的特征基因，这些基因对临床结果来说不太重要但极其重要。它还使我们能够消除与临床结果相关性稍高但总体较弱的冗余分子。随着特征数量肯定减少到可管理的水平，将引入惩罚伪似然方法来进一步选择相关基因。此外，还介绍了寻找分子协同基团的方法。独立筛选的思想及其迭代版本将应用于从高通量数据分析到各种统计问题，从超高维回归和分类到生存时间分析、全基因组方差估计和微阵列标准化。所提出方法的有效性将通过渐近理论和模拟研究进行评估。将使用新开发的统计和生物信息学工具对正在进行的乳腺癌、多发性骨髓瘤、神经母细胞瘤、肺肿瘤和肝癌等癌症生物医学研究的数据集进行严格分析。