Statistical Methods for RNA-seq Data Analysis

项目摘要 传统的rna-seq研究从散装样本中收集rna-seq数据(散装rna-seq)，从而将 来自多种细胞类型的信号。不同样本之间基因表达的差异可能是由于细胞的差异 类型组成或细胞类型特定的基因表达，以及大量的rna-seq数据不能区分这两者。 各种因素。事实上，细胞类型特定的信号可能会被大量的RNA-seq数据屏蔽甚至错误地表示。单人 细胞RNA测序(scRNA-seq)可以克服散装RNA-seq的部分局限性。但是，在一个 在可预见的未来，由于成本和后勤障碍，它不能应用于大量队列。在本R01中 建议，我们提出了新的统计/计算方法来研究细胞类型组成或特定细胞类型 使用批量RNA-seq数据、scRNA-seq数据或同时使用批量RNA-seq和scRNA-seq数据的基因表达。这 该方法可以有效地利用现有的海量RNA-seq数据，并可以带来范型-- 将更改转移到多个字段，例如，识别与疾病特性相关联的细胞类型或定义 使用细胞类型特异性基因表达的新生物标记物。我们计划实现以下三个具体目标。 在目标1中，我们提出了针对特定细胞类型的差异表达分析的新方法以及 评估细胞类型组成和感兴趣的协变量之间的关联。在目标2中，我们将重点放在 细胞类型特异性基因表达与生殖系遗传变异之间的关系，即研究细胞类型- 特异基因表达的数量性状基因座(EQTL)。在目标3中，我们研究了体细胞之间的联系 突变和细胞类型组成或细胞类型特定的基因表达。

Project Summary Traditional RNA-seq studies collect RNA-seq data from bulk samples (bulk RNA-seq) and thus aggregate the signals from multiple cell types. Gene expression variation across samples may be due to difference of cell type composition or cell type-specific gene expression, and bulk RNA-seq data cannot distinguish these two factors. In fact, cell type-specific signals may be masked or even misrepresented by bulk RNA-seq data. Single cell RNA-sequencing (scRNA-seq) may overcome part of the limitations of bulk RNA-seq. However, in a foreseeable future, it cannot be applied to a large cohort due to cost and logistical barriers. In this R01 proposal, we propose new statistical/computational methods to study cell type composition or cell type-specific gene expression using bulk RNA-seq data, scRNA-seq data, or both bulk RNA-seq and scRNA-seq data. This approach can effectively exploit the huge amount of existing bulk RNA-seq data, and it can bring paradigm- shifting changes to many fields, for example, identifying cell types associated with a disease trait or defining new biomarkers using cell type-specific gene expression. We plan to achieve the following three specific aims. In Aim 1, we propose novel methods for cell type-specific differential expression analysis as well as methods to assess the association between cell type composition and covariates of interest. In Aim 2, we focus on the association between cell type-specific gene expression and germline genetic variants, i.e., studying cell type- specific gene expression quantitative trait loci (eQTLs). In Aim 3, we study the association between somatic mutations and cell type composition or cell type-specific gene expression.