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

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


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Autocrine Communication
  • Humans
  • Paracrine Communication
  • RNA, Small Cytoplasmic / genetics*
  • Sequence Analysis, RNA / methods*


  • RNA, Small Cytoplasmic