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: 0803531
• 负责人: Anindya Roy
• 金额: $ 8.11万
• 依托单位: University of Maryland Baltimore County
• 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 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0803531&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.

多个测试问题的统计分析围绕同时测试引起的P值的分布分布。 fMRI，蛋白质组学，微阵列和其他生物医学实验的数据在P值之间表现出依赖性。如果未考虑这种依赖性，则在估计错误控制措施（例如错误发现率）时，统计推断在生物学上产生无关紧要的结论。该提案通过使用混合物在依赖情况下对P值的联合分布进行柔性，准确的建模来描述一种以模型为导向的方法来测试多个假设。一个额外的理论目标研究人员研究偏斜模型的特性。通过将依赖性纳入对P值的模型，拟议的研究提供了对错误发现的有效控制，尤其是在复杂的生物医学应用中。所提出的方法为在错误发现控制领域的大型多重测试情况下为统计分析提供了基础。多种假设测试是科学家可用的主要统计工具之一，用于有效地分析大型复杂生物学数据，例如基因 - 基因表达数据，原始数据或大脑数据或大脑数据。这种生物医学应用中的疾病关联研究需要同时测试数千个基因或蛋白质或大脑区域的关联的重要性。鉴定基因或蛋白质可能与给定疾病相关的蛋白质称为发现。但是，在大规模的生物医学研究中，通过做出过多的错误发现存在积累错误的风险。拟议的研究通过在大规模疾病关联研究中提供准确的错误率估计来实质性地影响生物医学应用中的统计实践。研究人员专门为自闭症患者的MRI研究中的大脑成像应用提供了错误控制机制。 该项目以研究生教育和培训的形式影响人力资源的发展。