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病变细胞中的异常分子改变 呈现改变的信号通路和疾病相关细胞群的出现，导致多- 在疾病组织微环境中缩放细胞结构。然而，缺点如低测序 每个小区的深度、昂贵的成本和跨小区信令信息的丢失阻碍了其更广泛的应用。 适用于大规模群体。相反，临床上界限分明、多模态和高深度的大块- 基于测序的数据在公共领域中大量可用，并且可以用于组装鲁棒的 遗传疾病的分子模型，并推断样品中的细胞群体丰度。特别是， 网络生物学方法已经有效地整合了复杂的大规模和多样化的生物医学数据集， 人类疾病，并剖析疾病机制和新的治疗策略。因此，一个系统的方法， 迫切需要协同地利用批量和单细胞测序数据这些互补方面， 构建疾病机制的强大分子模型，同时解决细胞的多尺度性质 病变组织中的结构。首先，我们将系统地研究多尺度细胞架构， 开发了一种新的无监督细胞聚类方法，单细胞递归多尺度聚类，通过局部 嵌入（scRECIEM）。在scRECIEM中，将开发一种新的单元-单元网络构建算法 通过将每个单元与其最近邻单元嵌入到拓扑球面上， 当并行化时，复杂度与细胞的数量成线性比例。这将伴随着一个顶部- 向下分裂聚类方法，在每次分裂时自适应地利用信息特征，该方法由 网络紧性测度，α。这些将识别在不同位置捕获的细胞簇的层次结构 决议。其次，我们将开发综合多尺度网络分析（iMUSNET）框架， 利用上下文匹配块构建疾病病因学的数据驱动和机制网络模型 样品在iMUSNET中，将系统地分析批量和单细胞队列的上下文匹配对。 收集，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 在不同的决议。这些基因模块将进行测试，以丰富与临床纲要， 在批量队列中策划的基因组基因签名。关键驱动因素分析将系统地寻找 通过利用网络模型拓扑结构来确定临床基因组签名的潜在上游调节器。 此外，我们将通过利用 scRECITE推断的细胞增殖，这些将告知相关的疾病相关的细胞群， 批量队列。总的来说，iMUSNET将产生一些可测试的假设作为潜在的监管机构， 潜在的感兴趣的疾病的子网络。

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.