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

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-a

SUBMITTER: Tyler SR 

PROVIDER: S-EPMC6394844 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature