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

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

PROVIDER: PRJNA480045 | ENA |

REPOSITORIES: ENA

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

Project description: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.

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