PyMINEr Finds Gene and Autocrine/Paracrine Networks from Human Islet scRNAseq
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA480045 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7493780_1.fastq.gz | Fastqsanger.gz | |||
| SRR7493780_2.fastq.gz | Fastqsanger.gz | |||
| SRR7493790_1.fastq.gz | Fastqsanger.gz | |||
| SRR7493790_2.fastq.gz | Fastqsanger.gz | |||
| SRR7493800_1.fastq.gz | Fastqsanger.gz |
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Project description: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.
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