Statistical Methods for Elucidating Regulatory Mechanisms and Functional Impacts of Transcriptome Variation at Population and Single-Cell Scales

项目总结/摘要 批量RNA测序（RNA-seq）和单细胞RNA测序（scRNA-seq）是强大的高通量 在群体和单细胞尺度上研究转录组变异的技术。许多计算 已经开发了用于分析大量RNA-seq和scRNA-seq数据的方法。然而，仍有多个 从人群规模的批量RNA-seq数据中识别疾病/性状相关基因的挑战，研究 来自scRNA-seq数据的时间转录组动态，以及基准scRNA-seq计算工具。在 我们提出的研究，我们将开发统计方法来解决这些挑战，并阐明监管 在群体和单细胞尺度上的转录组变异机制。在人口规模上，我们将 建立一个统一的统计框架，以确定基因型和RNA亚型之间的关联 丰度，“理想的”RNA水平的分子表型。我们的框架将统一现有的各种方法 其集中于转录物变异的特定方面（例如，基因表达，选择性外显子/内含子使用，和 替代的多聚腺苷酸化），并且第一次将不确定性纳入估计同种型丰度。 因此，我们的框架应该提高检测遗传之间关联的准确性和能力。 变体和基因。我们将使我们的框架适用于所有第二代和第三代RNA-seq数据 并将其应用于最全面的基因型转录组数据库GTEx数据，以发现基因 与GWAS发现的疾病/性状相关变异相关的基因。在单细胞规模，我们将 发展了三种方法：1）有效的统计检验，用于检测时间差异表达的基因， scRNA-seq数据，同时考虑轨迹推断中的不确定性，2）聚类方法， 整合了用于沿时间过程沿着鉴定细胞亚群的机制和统计建模，以及 3)一个全面和可解释的模拟器，可生成真实的scRNA-seq数据，用于基准测试 计算工具。前两种方法将为时间基因表达提供急需的解决方案 scRNA-seq数据的分析。他们的应用将包括巨噬细胞转录组变化的研究 在免疫反应中。第三种方法将是第一个可扩展和透明的模拟器， 基因相关性，并允许调整实验参数，包括细胞数量和文库大小。 总的来说，我们希望我们提出的方法将显着提高功率，鲁棒性， 从批量和单细胞RNA-seq数据研究转录组变异的可重复性。

PROJECT SUMMARY / ABSTRACT Bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) are powerful high-throughput techniques for studying transcriptome variation at population and single-cell scales. Many computational methods have been developed for analyzing bulk RNA-seq and scRNA-seq data. However, there remain multiple challenges in identifying disease/trait-associated genes from population-scale bulk RNA-seq data, studying temporal transcriptome dynamics from scRNA-seq data, and benchmarking scRNA-seq computational tools. In our proposed research, we will develop statistical methods to address these challenges and elucidate regulatory mechanisms of transcriptome variation at population and single-cell scales. At the population scale, we will develop a unified statistical framework for identifying associations between genotypes and RNA isoform abundances, the “ideal” RNA-level molecular phenotypes. Our framework will unify existing diverse approaches that focus on specific aspects of transcript variation (e.g., gene expression, alternative exon/intron usage, and alternative polyadenylation) and, for the first time, incorporate the uncertainty in estimating isoform abundances. As a result, our framework should improve the accuracy and power in detecting associations between genetic variants and genes. We will make our framework applicable to all second- and third-generation RNA-seq data and apply it to the GTEx data, the most comprehensive genotype-transcriptome database, to discover genes that are associated with the disease/trait-associated variants found by GWAS. At the single-cell scale, we will develop three methods: 1) a valid statistical test for detecting temporally differentially expressed genes from scRNA-seq data while accounting for the uncertainty in trajectory inference, 2) a clustering method that integrates mechanistic and statistical modeling for identifying cell subpopulations along a temporal process, and 3) a comprehensive and interpretable simulator that generates realistic scRNA-seq data for benchmarking computational tools. The first two methods will offer much-in-demand solutions to temporal gene expression analysis of scRNA-seq data. Their applications will include the study of macrophage transcriptome changes during immune responses. The third method will be the first scalable and transparent simulator that captures gene correlations and allows the tuning of experimental parameters, including cell numbers and library sizes. Overall, we expect that our proposed methods will significantly improve the power, robustness, and reproducibility of studying transcriptome variation from bulk and single-cell RNA-seq data.