Hierarchical Testing

分层测试

• 批准号: 1162538
• 负责人: Susan Holmes
• 金额: $ 30万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-15 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1162538&HistoricalAwards=false
• 关键词: Hierarchical; Testing

Evaluation of biological data often needs statistical insight to detect whether apparent treatment effects are real and useful. Typical applications are to personalized medicine and drug resistance. Standard statistical methods rely on unrealistic assumptions (data is supposed to be independent and identically distributed). This project will provide biologists with new tools for detecting, quantifying and leveraging hierarchical dependencies in areas of microbiology currently revolutionized by the emergence of new sequencing technologies. The PIs propose to tailor new ``treeness'' and ``clustering'' indices incorporating relevant distance and structural information computed from sequence and contingent information. The investigators will also use the treeness indices to provide improved multiple testing programs that improve the power of corrected multiple testing procedures in the case of hierarchical dependencies between variables. This will enhance the power in detecting significant functional differences between different conditions. The methods will first be developed and calibrated on data simulated according to known tree structures. Calibration will evaluate the indices under various types of perturbations and thresholding. The methods will then be used on real data generated, as part of the proposed work by directed evolution experiments in microbial ecology.This work is an application-driven project for providing useful multiple testing correction under hierarchical dependencies. The goal is to tailor statistical methods to the exact needs of biologists working in bacterial ecology and in HIV/HCV drug resistance. This project provides the integration of a broad range of cutting edge mathematics, probability and statistics with computational advances that cater to the realities of data collection and analyses in the fields of phylogenetics, metatranscriptomics and metagenomics. Advances in the study of evolution, in microbial ecosystems (the human gut, sewage treatment plants) or virus evolution (HCV/HIV in a human host) would have repercussions on overall health practices at both the individual and epidemiological levels. Quantitative estimates of confidence in `entero-types' or other inferred clusters would be important in the cost analysis of personalized medicine. Students and Postdoctoral fellows will be trained both in biology and statistics, so they can understand the biologist's requests and constraints. Consulting workshops will be organized regularly where the effectiveness of planned experiments and applied statistics can be discussed. During the academic year, classes targeted to molecular biologists and microbiologists teach multivariate visualization and geometrical statistics methods using R. These will be open source and available from the class web pages. The PIs will offer several Summer schools in Microbiology and Metagenomics where they teach both multivariate statistics, phylogenetic analyses, metagenomic analysis, metatranscriptomics as well as experimental techniques for studying evolution in action.

对生物学数据的评估通常需要统计洞察力来检测明显的治疗效果是否真实和有用。典型的应用是个性化医疗和耐药性。标准的统计方法依赖于不切实际的假设（数据应该是独立和均匀分布的）。该项目将为生物学家提供新的工具，用于检测，量化和利用微生物学领域的分层依赖关系，目前由新的测序技术的出现带来了革命性的变化。pi建议定制新的“树度”和“聚类”指数，结合从序列和偶然信息计算的相关距离和结构信息。研究人员还将使用树度指数来提供改进的多重测试程序，以提高在变量之间的层次依赖性的情况下纠正多重测试程序的能力。这将增强检测不同条件之间显著功能差异的能力。这些方法将首先根据已知树结构模拟的数据进行开发和校准。校准将评估各种扰动和阈值下的指标。然后，这些方法将用于产生的实际数据，作为微生物生态学中定向进化实验提出的工作的一部分。这项工作是一个应用程序驱动的项目，用于在分层依赖关系下提供有用的多重测试校正。目标是根据从事细菌生态学和艾滋病毒/丙型肝炎病毒耐药性研究的生物学家的确切需要调整统计方法。该项目将广泛的前沿数学、概率和统计学与计算进步相结合，以满足系统发育学、元转录组学和元基因组学领域数据收集和分析的现实。进化研究、微生物生态系统（人类肠道、污水处理厂）或病毒进化（人类宿主体内的丙型肝炎病毒/艾滋病毒）方面的进展将对个人和流行病学层面的总体卫生做法产生影响。在个性化医疗的成本分析中，对“肠道类型”或其他推断群集的置信度进行定量估计将是重要的。学生和博士后将接受生物学和统计学方面的培训，这样他们就能理解生物学家的要求和限制。将定期组织咨询讲习班，讨论计划实验和应用统计的有效性。在学年期间，针对分子生物学家和微生物学家的课程将使用r教授多元可视化和几何统计方法。这些课程将是开源的，可以从课程网页上获得。pi将开设几所微生物学和宏基因组学暑期学校，教授多元统计学、系统发育分析、宏基因组学分析、元转录组学以及研究实际进化的实验技术。