Hierarchical Testing

对生物数据的评估通常需要有统计洞察力，以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药物和耐药性。标准的统计方法依赖于不切实际的假设(数据应该是独立的、同分布的)。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中目前因新测序技术的出现而发生革命性变化的层级依赖关系。投资促进机构建议调整新的“树形”和“聚类”指数，纳入根据序列和或有信息计算出的相关距离和结构信息。调查人员还将使用树指数来提供改进的多重测试程序，以提高在变量之间存在层次依赖的情况下修正的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。这些方法将首先在根据已知树结构模拟的数据上开发和校准。校准将评估在各种类型的扰动和阈值下的指数。然后，这些方法将用于生成的真实数据，作为微生物生态学定向进化实验的拟议工作的一部分。这项工作是一个应用程序驱动的项目，用于在分层依赖下提供有用的多重测试校正。其目标是根据在细菌生态学和艾滋病毒/丙型肝炎病毒耐药性方面工作的生物学家的确切需求定制统计方法。该项目将广泛的尖端数学、概率和统计学与计算进步结合起来，以迎合系统发育学、后基因组学和元基因组学领域的数据收集和分析的现实。在微生物生态系统(人体肠道、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)的进化研究方面的进展将在个人和流行病学层面上对整体卫生做法产生影响。在个体化药物成本分析中，对“肠型”或其他推断类群的置信度的定量估计将是重要的。学生和博士后研究员将接受生物学和统计学方面的培训，这样他们就可以理解生物学家的要求和限制。将定期举办咨询讲习班，讨论计划中的实验和应用统计的有效性。在学年中，针对分子生物学家和微生物学家的课程将使用R教授多变量可视化和几何统计方法。这些课程将是开源的，可以从课程的网页上获得。PIS将开设几个微生物学和元基因组学暑期班，教授多元统计学、系统发育分析、元基因组分析、元转录组学以及研究实际进化的实验技术。

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.