Integrative network modeling of bulk and single-cell sequencing data to characterize multi-scale cell architecture

摘要 单细胞测序技术使我们能够识别疾病细胞中的异常分子变化 呈现改变的信号通路和疾病相关细胞群的出现，导致多- 疾病组织微环境中的尺度细胞结构。然而，诸如低排序等缺点 每个信元的深度、昂贵的成本和跨信元信令信息的丢失阻碍了其更广泛的应用 对大规模队列的适用性。相反，临床上定义明确、多模式和高深度的批量- 基于测序的数据在公共领域中大量可用，并可用于组装健壮的 遗传病的分子模型，并推断样本中的细胞种群丰度。尤其是， 网络生物学方法已经有效地集成了复杂的大规模和多样化的生物医学数据集 人类疾病，并剖析其发病机制和新的治疗策略。因此，一种系统的方法来 协同利用大量和单细胞测序数据的这些互补方面是迫切需要的 构建强大的疾病机制的分子模型，同时解决细胞的多尺度性质 病变组织中的结构。首先，我们将通过以下方式系统地研究多尺度单元架构 提出一种新的无监督细胞聚类方法--基于局部的单细胞递归多尺度聚类 嵌入(ScRECIEM)。在SCRECIEM中，将开发一种新的小区-小区网络构建算法 通过将每个单元与其最近的相邻单元嵌入到拓扑球上，并进行产生式计算 当并行化时，复杂性与单元数成线性关系。这将伴随着一件上衣- 向下分裂的聚类方法，在每次拆分时自适应地利用信息特征，这是由 网络紧凑度，υ(α)。这些将识别在不同的时间捕获的细胞团的层次结构 决心。其次，我们将开发综合多尺度网络分析(IMUSNET)框架，以 利用上下文匹配的Bulk构建数据驱动和机械的疾病病因学网络模型 样本。在iMUSNET内，批量和单细胞队列的上下文匹配对将系统地 我们将构建多尺度的基因相互作用网络，捕获不同的共表达模块 以不同的分辨率。这些基因模块将用临床简编进行富集性测试。 基因组基因签名在大量队列中进行整理。关键驱动因素分析将系统地寻找 通过利用网络模型拓扑对临床基因组签名的潜在上游调节器。 此外，我们将利用上下文匹配的单细胞簇的丰度 单斜晶石推断的细胞系统发育，这些将告知相关的疾病相关细胞群体在 散装队列。总体而言，iMUSNET将产生一些可测试的假设，作为潜在的监管机构和 潜在的感兴趣疾病的子网络。

Abstract Single cell sequencing technology enabled us to identify the aberrant molecular alterations in diseased cells presenting altered signaling pathways and emergence of disease-associated cell populations, leading to multi- scale cell architectures in disease tissue micro-environment. However, drawbacks such as low sequencing depth per cell, expensive costs and loss of cross-cell signaling information have hindered its broader applicability to large-scale cohorts. On the contrary, clinically well-defined, multi-modal and high-depth bulk- based sequencing data are abundantly available in public domain, and can be utilized to assemble robust molecular models of the genetic diseases, and infer cell population abundances in the samples. Especially, network biology approaches have been effective for integrating large-scale and diverse biomedical datasets in complex human diseases, and dissect the disease mechanisms and novel therapeutic strategies. Thus, a systems approach to synergistically utilize these complementary aspects of bulk and single-cell sequencing data is urgently needed to construct the robust molecular models of disease mechanisms while addressing the multi-scale nature of cell architectures in diseased tissues. Firstly, we will systematically investigate multi-scale cell architectures by developing a novel unsupervised cell clustering approach, single-cell recursive multi-scale clustering via local embedding (scRECIEM). Within scRECIEM, a novel cell-cell network construction algorithm will be developed by embedding each cell with its nearest neighboring cells on topological sphere, and yield computation complexity that linearly scales with the number of cells when parallelized. This will be accompanied by a top- down divisive clustering approach that adaptively utilizes informative features at each split, which is guided by network compactness measure, υ(α). These will identify a hierarchy of cell clusters captured at different resolutions. Secondly, we will develop integrative multi-scale network analysis (iMUSNET) framework to construct data-driven and mechanistic network models of disease etiology by utilizing the context-matched bulk samples. Within iMUSNET, the context-matched pairs of bulk and single-cell cohorts will be systematically collected, and we will construct multi-scale gene interaction networks capturing diverse co-expressed modules at different resolutions. These gene modules will be tested for enrichments with a compendium of clinico- genomic gene signatures curated within the bulk cohort. Key driver analysis will systematically look for potential up-stream regulators of the clinic-genomic signatures by leveraging the network model topology. Further, we will infer abundances of the context-matched single-cell clusters with high accuracy by utilizing the scRECITE-inferred cell phylogeny, and these will inform relevant disease associated cell populations in the bulk cohort. Overall, iMUSNET will generate a number testable hypotheses as potential regulators and subnetworks underlying the disease of interest.