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: 10707494
• 负责人: Yang Ni
• 金额: $ 29.79万
• 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/10707494
• 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; Predisposition; Prevention; Procedures; Regulator Genes; Research; Sample Size; Sampling; Technology; Testing; Time; Tissues; Translating; Uncertainty; Work; causal variant; cell type; differential expression; disease diagnosis; experimental group; gene regulatory network; improved; network models; 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分因果网络和聚类方法 意义 A.1 待解决问题的重要性 单细胞 RNA 测序 (scRNA-seq) 技术促进了新的生物学发现 批量 RNA-seq 是不可能实现的，例如在单细胞水平发现新的基因调控活性， 然而，为了转化 scRNA 先进的基础生物学知识， 为了改善疾病的诊断、治疗和预防，需要新的方法来相对 研究正常和病理细胞/组织之间以及对照和组织之间的分子差异 尽管鉴定了两个样本组之间的差异表达基因。 迄今为止，绝大多数现有的识别基因调控的方法已经得到了广泛的研究。 到目前为止，实验室网络（GRN）和细胞类型主要关注在单一网络下生成的 scRNA-seq 数据。 原则上，这些方法一次可以应用于一种实验条件， 在此基础上可以进行事后比较，以发现实验造成的差异 然而，与联合建模方法相比，这种两步程序被认为较少。 由于缺乏适当的不确定性传播，效率更高并且更容易受到错误发现的影响 此外，大多数 scRNA-seq 网络模型本质上是相关的，并且不相关。 因此，迫切需要开发新的模型来推断因果基因调控关系。 通过联合确定实验干预对因果基因调控和细胞组成的影响 在缺乏此类工具的情况下，机械地对跨实验组的 scRNA-seq 数据进行建模。 了解基因调控和细胞分化，充分实现scRNA-seq的翻译价值 研究可能仍然很困难。 A.2 先前研究的严谨性 目标 1. 许多现有的 scRNA-seq 网络方法将标准关联测量方法调整为零 夸大的 scRNA-seq 数据，例如 Pearson 相关性 [1] 和互信息 [2]。 这些方法的一个特点是它们仅量化边际依赖性，这很容易受到虚假间接的影响 处理条件关联的图形模型是强大的替代方案。 出于通用目的，人们提出了许多方法[4, 5]。 包括非高斯数据的开发 [6-9]，特别是 scRNA-seq 数据，两个无向数据。 最近提出了基于邻域选择的图形模型，包括 Co-I Cai 的工作 [10, 11] 然而，这并不能推断因果基因调控。为了确定因果关系，有几种选择。 开发了方法 [12, 13] 然而，这些方法要么忽略了 scRNA-seq 的计数性质。 数据，需要已知的伪时间（在真实的 scRNA-seq 数据中很少知道），或者理论上不需要 对于差分网络，有许多方法可以研究横截面观察的因果可识别性。 [14-18] 包括 PI 之前的工作 [19] 已经针对批量 RNA-seq 数据进行了开发，这些数据显示出很好的效果 联合分析相对于独立分析的优点然而，存在的微分网络要少得多。 scRNA-seq 数据的工作方法，例如 PT [20] 和 scdNet [21]。 scdNet 的缺点是他们只考虑边际依赖性（因此容易受到错误发现的影响）并且不考虑 发现因果关系。我们的初步结果 (§C.1) 表明所提出的贝叶斯模型。 网络模型能够识别横截面 scRNA-seq 中的因果基因调控关系 数据，并且通常优于最先进的替代方法。 目标 2. 构建细胞特异性网络的方法很少，因为很难估计 最近，开发了一种假设检验方法 [22]。 估计特定于小区的网络。该方法基于每个小区进行近似网络推断。 然而，它只考虑对称（无向）边际依赖，因此 无法推断因果监管关系，并且容易受到 PI 先前工作的虚假关联的影响。 [23] 解决了批量 RNA-seq 数据中的“样本大小一”问题，假设因果网络是 附加协变量的平滑函数但是，如果没有协变量，则该方法不适用。 不允许反馈循环，这是 GRN 中的一个常见主题 [24, 25]，包括 PI 的 [19]。 1