Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA; College of Engineering, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
Cell Rep. 2019 Feb 12;26(7):1951-1964.e8. doi: 10.1016/j.celrep.2019.01.063.
Toolsets available for in-depth analysis of scRNA-seq datasets by biologists with little informatics experience is limited. Here, we describe an informatics tool (PyMINEr) that fully automates cell type identification, cell type-specific pathway analyses, graph theory-based analysis of gene regulation, and detection of autocrine-paracrine signaling networks in silico. We applied PyMINEr to interrogate human pancreatic islet scRNA-seq datasets and discovered several features of co-expression graphs, including concordance of scRNA-seq-graph structure with both protein-protein interactions and 3D genomic architecture, association of high-connectivity and low-expression genes with cell type enrichment, and potential for the graph structure to clarify potential etiologies of enigmatic disease-associated variants. We further created a consensus co-expression network and autocrine-paracrine signaling networks within and across islet cell types from seven datasets. PyMINEr correctly identified changes in BMP-WNT signaling associated with cystic fibrosis pancreatic acinar cell loss. This proof-of-principle study demonstrates that the PyMINEr framework will be a valuable resource for scRNA-seq analyses.
生物学家在进行 scRNA-seq 数据集的深入分析时，可用的工具集非常有限，而这些工具集通常缺乏计算生物学经验。在这里，我们描述了一种计算工具（PyMINEr），它可以全自动地进行细胞类型识别、细胞类型特异性途径分析、基于图论的基因调控分析以及自动分泌-旁分泌信号网络的计算检测。我们应用 PyMINEr 来分析人类胰岛 scRNA-seq 数据集，并发现了共表达图的几个特征，包括 scRNA-seq 图结构与蛋白质-蛋白质相互作用和 3D 基因组结构的一致性、高连接性和低表达基因与细胞类型富集的关联，以及图结构阐明神秘疾病相关变异潜在病因的潜力。我们进一步在来自七个数据集的胰岛细胞类型内和跨细胞类型创建了共识共表达网络和自动分泌-旁分泌信号网络。PyMINEr 正确识别了与囊性纤维化胰腺腺泡细胞丢失相关的 BMP-WNT 信号变化。这项原理验证研究表明，PyMINEr 框架将成为 scRNA-seq 分析的有价值资源。
