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

SUMMARY 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.
Graphical Abstract
In Brief Tyler et al. create PyMINEr, an open-source program (https://www.sciencescott.com/pyminer) that automates analyses of expression datasets without coding. These analyses include clustering, differential expression, pathway analyses, co-expression networks, marker gene identification, and autocrine-paracrine signaling prediction. Integration of seven datasets shows elevated BMP-WNT signaling in cystic fibrosis pancreata.