Collaborative Research: Detecting false discoveries under dependence using mixtures

合作研究：使用混合物检测依赖性下的错误发现

• 批准号: 0803540
• 负责人: Subhashis Ghoshal
• 金额: $ 6万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0803540&HistoricalAwards=false
• 关键词: Collaborative; Research; Detecting; false; discoveries

Statistical analysis of multiple testing problems revolves around the distribution of the collection of p-values arising from simultaneous tests. Data from fMRI, Proteomics, Microarray and other biomedical experiments exhibit dependence among p-values. Statistical inference yields biologically irrelevant conclusions if such dependence is not taken into consideration while estimating error control measures such as the false discovery rate. This proposal delineates a model oriented approach to multiple hypotheses testing by flexible and accurate modeling of the joint distribution of the p-values in dependent situations using mixtures. An additional theoretical goal the investigators study properties of skew-mixture models. By incorporating dependence in the model for the p-values, the proposed research provides valid controls of false discoveries, especially in complex biomedical applications. The proposed methodologies provide a foundation for statistical analysis in large dependent multiple testing situations and will spawn new research in the area of false discovery control.Multiple hypothesis testing is one of the primary statistical tools available to the scientists for efficiently analyzing large-scale complex biomedical data such as gene-expression data, protemics data or brain imaging data. Disease association studies in such biomedical applications require testing significance of association of several thousand genes or proteins or brain regions, simultaneously. Identification of a gene or a protein as being potentially associated with a given disease is called a discovery. However, in large scale biomedical studies there is a risk of accumulating error via making too many false discoveries. The proposed research substantially influences the practice of statistics in biomedical applications by providing accurate estimates of error rates in large scale disease association studies. The investigators specifically develop error control mechanism for brain imaging applications in MRI studies of autistic patients. The project impacts human resource development in the form of graduate student education and training.

多重检验问题的统计分析围绕着同时检验产生的p值集合的分布展开。功能磁共振成像（fMRI）、蛋白质组学（Proteomics）、微阵列（Microarray）和其他生物医学实验的数据显示p值之间存在相关性。如果在估计错误控制措施（如错误发现率）时不考虑这种依赖性，统计推断会产生生物学上无关的结论。这一建议描绘了一个模型导向的方法，通过灵活和准确的建模的联合分布的p值在依赖情况下使用混合物多假设检验。研究者的另一个理论目标是研究倾斜混合模型的性质。通过将p值的依赖性纳入模型，提出的研究提供了对错误发现的有效控制，特别是在复杂的生物医学应用中。所提出的方法为大规模依赖的多重测试情况下的统计分析提供了基础，并将在错误发现控制领域产生新的研究。多重假设检验是科学家有效分析大规模复杂生物医学数据（如基因表达数据、蛋白质数据或脑成像数据）的主要统计工具之一。此类生物医学应用中的疾病关联研究需要同时检测数千个基因或蛋白质或大脑区域的关联意义。发现与某种疾病有潜在联系的基因或蛋白质被称为发现。然而，在大规模的生物医学研究中，由于有太多的错误发现，存在累积错误的风险。拟议的研究通过提供大规模疾病关联研究错误率的准确估计，实质性地影响了生物医学应用中的统计实践。针对自闭症患者MRI研究中脑成像的应用，研究人员专门开发了错误控制机制。该项目以研究生教育和培训的形式影响人力资源开发。