PyMINEr Finds Gene and Autocrine/Paracrine Networks from Human Islet scRNAseq

Toolsets available for in-depth analysis of scRNAseq 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 scRNAseq datasets and discovered several features of co-expression graphs including: concordance of scRNAseq-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 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 7-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 scRNAseq analyses. Human islets were obtained from the integrated islet distribution program (IIDP), cultured overnight, then prepared for scRNAseq via the Fluidigm C1 platform. RNAseq was perfromed on Illumina HiSeq 2500.