NB Regression and HOA Inference for RNA-Seq Gene Expression Analysis

描述（由申请人提供）：生物学家正在迅速采用RNA测序（RNA-Seq）来研究转录组，以基本了解细胞功能，并解决食品生产，食品安全，制药，人类健康，疾病治疗和疾病预防等领域的重要需求。然而，用于完整和具体分析RNA-Seq数据的统计工具出现缓慢，并且为其他应用开发的现成工具的使用具有产生误导性结论的强大潜力。Germane说，为了实现这一建议的目标，基于两组比较的负二项（NB）精确检验，用于评估RNA-Seq差异基因表达的复杂方法尚未扩展到回归分析。这种方法需要评估差异基因表达后，会计协变量，分析依赖的解释变量的表达，并研究多个因素的表达的相互作用。本提案的目标是以下列方式满足这一需要：1）开发、评估和实施对用于RNA-Seq数据的NB回归分析的似然比推断的高阶渐近（HOA）调整，包括制备用于RNA-Seq数据的完全回归分析的公开可用的R包，以及将推断计算包括在已经公开可用的R包中，基于Perl的计算管道，用于完整分析RNA-Seq数据; 2）阐明RNA-Seq研究的最佳推理能力，并提供用于评估样本量需求的计算机程序; 3）开发交互式动态可视化程序，用于传达RNA-Seq数据，NB回归模型结果和相关的不确定性。所使用的方法包括高阶渐近理论的应用，蒙特卡罗模拟，详细程度（LOD）的“焦点加上下文”可视化方法的发展，并认真关注真实的RNA-Seq数据集。

DESCRIPTION (provided by applicant): Biologists are rapidly adopting RNA-Sequencing (RNA-Seq) to study transcriptomes for basic understanding of cellular functions and to address important needs in such areas as food production, food security, pharmacy, human health, disease treatment, and disease prevention. Statistical tools for complete and specific analysis of RNA-Seq data, however, have been slow to emerge, and the use of off-the-shelf tools developed for other applications has the strong potential to produce misleading conclusions. Germane to the goals of this proposal, sophisticated methods for assessing differential gene expression from RNA-Seq- based on a negative binomial (NB) exact test for two-group comparisons-have not yet been extended to regression analysis. Such methods are required for assessing differential gene expression after accounting for covariates, for analyzing the dependence of expression on explanatory variables, and for studying interactive effects on expression of multiple factors. The objectives of this proposal are to address this need in the following ways: 1) develop, assess, and implement higher-order asymptotic (HOA) adjustments to likelihood ratio inference for NB regression analysis of RNA-Seq data, including the preparation of a publicly-available R package for complete regression analysis of RNA-Seq data, and the inclusion of the inferential computations in an already publicly available, Perl-based computational pipeline for complete analysis of RNA-Seq data; 2) clarify the power of optimal inference for RNA-Seq studies and provide a computer program for assessing sample size needs; and 3) develop an interactive, dynamic visualization program for conveying RNA-Seq data, NB regression model results, and associated uncertainties. The methods used include the application of higher-order asymptotic theory, Monte Carlo simulation, the development of Level of Detail (LOD) "focus plus context" visualization methods, and serious attention to real RNA-Seq datasets.