标题： 与生物注释元数据相关的多重检验 显示英文标题

标题： Multiple tests of association with biological annotation metadata

摘要： 我们提出了一种通用且形式化的统计框架，用于检测已知的基因组固定特征与目标种群中可变特征分布的未知参数之间的多重关联测试。 已知的基因注释概况，对应于基因组的固定特征，可能涉及基因本体（GO）注释、通路成员资格、特定转录因子的调控、核苷酸序列或蛋白质序列。 未知的基因参数概况，对应于基因组的可变特征，例如，可能是将可能被删失的生物和临床结果与全基因组转录水平、DNA拷贝数和其他协变量相关联的回归系数。 当前基因组研究中一个非常感兴趣的问题是检测生物注释元数据与全基因组表达测量之间的关联。 这一生物学问题可以转化为关于基因注释概况与基因参数概况之间关联度量的多重假设检验。 对统计推断问题进行通用且严格的表述，使我们能够应用在[多重检验程序及其在基因组学中的应用（2008）Springer, New York]及相关文章中开发的多重假设检验方法，以控制一类广义尾部概率和任意函数的类型I误差数和被拒绝假设数的期望值定义的类型I误差率。 [多重检验程序及其在基因组学中的应用（2008）Springer, New York] 中基于重采样的单步和逐步多重检验程序考虑了检验统计量的联合分布，并在涉及一般数据生成分布（变量间具有任意依赖结构）、原假设和检验统计量的检验问题中提供了类型I误差控制。 显示英文摘要

摘要： We propose a general and formal statistical framework for multiple tests of association between known fixed features of a genome and unknown parameters of the distribution of variable features of this genome in a population of interest. The known gene-annotation profiles, corresponding to the fixed features of the genome, may concern Gene Ontology (GO) annotation, pathway membership, regulation by particular transcription factors, nucleotide sequences, or protein sequences. The unknown gene-parameter profiles, corresponding to the variable features of the genome, may be, for example, regression coefficients relating possibly censored biological and clinical outcomes to genome-wide transcript levels, DNA copy numbers, and other covariates. A generic question of great interest in current genomic research regards the detection of associations between biological annotation metadata and genome-wide expression measures. This biological question may be translated as the test of multiple hypotheses concerning association measures between gene-annotation profiles and gene-parameter profiles. A general and rigorous formulation of the statistical inference question allows us to apply the multiple hypothesis testing methodology developed in [Multiple Testing Procedures with Applications to Genomics (2008) Springer, New York] and related articles, to control a broad class of Type I error rates, defined as generalized tail probabilities and expected values for arbitrary functions of the numbers of Type I errors and rejected hypotheses. The resampling-based single-step and stepwise multiple testing procedures of [Multiple Testing Procedures with Applications to Genomics (2008) Springer, New York] take into account the joint distribution of the test statistics and provide Type I error control in testing problems involving general data generating distributions (with arbitrary dependence structures among variables), null hypotheses, and test statistics.