Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data

单细胞数据的贝叶斯差分因果网络和聚类方法

• 批准号: 10592720
• 负责人: Yang Ni
• 金额: $ 30.44万
• 依托单位: TEXAS A&M UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10592720
• 关键词: Address; Bayesian Modeling; Biological; Cell Differentiation process; Cells; Comparative Study; Data; Dependence; Development; Etiology; Feedback; Gene Expression Regulation; Genes; Intervention; Joints; Knowledge; Measures; Methods; Modeling; Molecular; National Institute of General Medical Sciences; Nature; Neighborhoods; Pathologic; Prevention; Procedures; Regulator Genes; Research; Sample Size; Sampling; Technology; Testing; Time; Tissues; Translating; Uncertainty; Work; base; causal variant; cell type; differential expression; disease diagnosis; experimental group; gene regulatory network; improved; network models; non-Gaussian model; single-cell RNA sequencing; tool; transcriptome sequencing; treatment group

Project Description DMS/NIGMS 2: Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data A Significance A.1 Importance of the Problem to Be Addressed Single-cell RNA-sequencing (scRNA-seq) technologies have facilitated new biological discoveries that were impossible with bulk RNA-seq, such as discovering at the single-cell level new gene regulatory activities and cell types. However, in order to translate the fundamental biological knowledge advanced by the scRNA- seq to improved disease diagnosis, treatment, and prevention, new methods are required to comparatively study the molecular differences between normal and pathological cells/tissues, and between control and case/treatment groups. Although identification of differentially expressed genes across two sample groups has been extensively studied, to date, the vast majority of the existing methods for identifying gene regu- latory networks (GRNs) and cell types have, so far, focused on scRNA-seq data generated under a single experimental condition. In principle, these methods can be applied to one experimental condition at a time, based on which post hoc comparisons can be made in order to find the differences caused by experimental interventions. However, compared to joint modeling approaches, this two-step procedure is deemed less efficient and more susceptible to false discoveries due to lack of proper uncertainty propagation from the first step to the second. Moreover, most scRNA-seq network models are correlative in nature and do not infer causal gene regulatory relationships. There is, therefore, a critical need to develop new models for identifying the effects of experimental interventions on causal gene regulation and cell composition by jointly modeling scRNA-seq data across experimental groups. In the absence of such tools, mechanistically un- derstanding gene regulation and cell differentiation, and fully realizing the translational values of scRNA-seq studies will likely remain difficult. A.2 Rigor of Prior Research Aim 1. Many existing scRNA-seq network approaches adapt standard association measures to zero- inflated scRNA-seq data, e.g. Pearson correlation [1] and mutual information [2]. A common limitation of these methods is that they only quantify marginal dependencies, which is susceptible to spurious indirect associations [3]. Graphical models which deal with conditional associations are powerful alternatives to the marginal association measures. Numerous methods have been proposed for general purposes [4, 5] including the development on non-Gaussian data [6–9]. Specifically for scRNA-seq data, two undirected graphical models including Co-I Cai's work [10, 11] were recently proposed based on neighborhood selec- tion which, however, do not infer causal gene regulation. To identify causal relationships, several alternative methods [12, 13] were developed. However, these methods either ignore the count nature of scRNA-seq data, require a known pseudotime (which is rarely known in real scRNA-seq data), or do not theoretically in- vestigate causal identifiability for cross-sectional observations. For differential networks, many approaches [14–18] including the PI's prior work [19] have been developed for bulk RNA-seq data which showed great advantages of joint analyses over independent analyses. However, there exist much fewer differential net- work methods for scRNA-seq data, e.g., PT [20] and scdNet [21] . The common limitation of PT and scdNet is that they only consider marginal dependence (hence susceptible to false discoveries) and do not discover causality. Results from our preliminary results (§C.1) demonstrate that the proposed Bayesian network model is capable of identifying causal gene regulatory relationships in cross-sectional scRNA-seq data and often outperforms the state-of-the-art alternative methods. Aim 2. Very few methods are available to construct cell-specific networks because it is difficult to estimate networks with, in essence, sample size one. Recently, a hypothesis testing approach [22] was developed to estimate cell-specific networks. The method makes approximate network inference of each cell based on its neighbors. However, it only considers symmetric (undirected) marginal dependence, and therefore cannot infer causal regulatory relationships and is susceptible to spurious associations. The PI's prior work [23] addressed the "sample-size-one" problem in bulk RNA-seq data assuming the causal networks are smooth functions of additional covariates. However, the method is not applicable without covariates and does not allow feedback loops, a common motif in GRN. Existing work [24, 25] including the PI's [19] has 1

项目描述 DMS/NIGMS 2：单细胞数据的贝叶斯差异因果网络和聚类方法 一个重要的能力 A.1要解决的问题的重要性 单细胞RNA - 序列（SCRNA-SEQ）技术已经制定了新的生物学发现 散装RNA-seq不可能，例如在单细胞水平上发现新基因调节活动和 细胞类型。但是，为了翻译scrna-提出的基本生物学知识 SEQ以改善疾病诊断，治疗和预防，需要使用新方法 研究正常细胞/组织之间的分子差异以及对照和对照之间 病例/治疗组。尽管在两个样本组中鉴定了不同表达的基因 到目前 到目前为止 实验条件。原则上，这些方法一次可以应用于一个实验条件， 基于可以进行哪些事后比较，以发现实验引起的差异 干预措施。但是，与关节建模方法相比，这种两步程序被认为较少 由于缺乏适当的不确定性传播，有效，更容易受到虚假发现的影响 第一步到第二步。此外，大多数SCRNA-SEQ网络模型本质上都是相关的，并且不 推断因果基因调节关系。因此，有至关重要的需要开发新模型 通过共同确定实验干预措施对因果基因调节和细胞组成的影响 在实验组之间对SCRNA-SEQ数据进行建模。在没有此类工具的情况下，机械上的 贫穷的基因调节和细胞分化，并充分意识到scrna-seq的翻译值 研究可能仍然很难。 A.2先前研究严格 目的1。许多现有的SCRNA-SEQ网络方法适应标准关联措施至零 插入的scrna-seq数据，例如Pearson相关[1]和共同信息[2]。一个共同的限制 这些方法是它们仅量化边缘依赖性，这容易受到伪造间接的影响 协会[3]。处理条件关联的图形模型是强大的替代方案 边缘关联措施。已经提出了许多用于一般目的的方法[4，5] 包括对非高斯数据的开发[6-9] .Specifinoscreccren-seq数据，两个无方向性 最近提出了基于邻里选择的图形模型[10，11] 但是，不推断因果基因调节。为了识别因果关系，几种替代方案 开发了方法[12，13]。但是，这些方法要么忽略scrna-seq的数量 数据需要已知的假频率（在实际scrna-seq数据中很少知道，或者在理论上不知道 横截面观察的物质身份能力。对于差异网络，许多方法 [14-18]包括PI的先前工作[19]已为散装RNA-Seq数据开发 联合分析比独立分析的优点。但是，差异网的存在很少 SCRNA-SEQ数据的工作方法，例如PT [20]和SCDNET [21]。 PT和 SCDNET是他们仅考虑边际依赖（因此容易受到虚假发现）而不考虑 发现休闲。我们的初步结果（§C.1）的结果表明，提议的贝叶斯人 网络模型能够鉴定横截面SCRNA-SEQ中的因果基因调节关系 数据，通常比最先进的替代方法优先。 AIM 2。几乎没有可用于构建细胞特异性网络的方法，因为它很难估算 从本质上讲，网络是样本尺寸一。最近，开发了一种假设检验方法[22] 估计细胞特异性网络。该方法使每个基于单元的网络推断近似网络推断 在邻居上。但是，它仅考虑对称（无方向性的）边际依赖性，因此 不能推断因果关系关系，并且容易受到虚假关联的影响。 PI的先前工作 [23]假设因果网络为，解决了批量RNA-seq数据中的“样本大小”问题 其他协变量的平滑功能。但是，没有协变量，该方法不适用于 不允许反馈循环，这是GRN中的常见主题。现有工作[24，25]，包括Pi [19] 1