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. Overall design: 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.