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Cell Rep. 2019 Feb 12;26(7):1951-1964.e8. doi: 10.1016/j.celrep.2019.01.063.

PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq.

Author information

1
Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA. Electronic address: scottyler89@gmail.com.
2
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.
3
Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA; Center for Gene Therapy, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
4
Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
5
Institute for Vision Research, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
6
Center for Gene Therapy, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
7
Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA; Center for Gene Therapy, University of Iowa Carver College of Medicine, Iowa City, IA, USA. Electronic address: john-engelhardt@uiowa.edu.

Abstract

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.

KEYWORDS:

BMP; PyMINEr; WNT; autocrine-paracrine; cell type identification; cystic fibrosis; networks; pancreatic islets; single-cell RNA-seq; systems biology

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