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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
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PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq.

Scott R Tyler1, Pavana G Rotti2, Xingshen Sun3

  • 1Department of Anatomy and Cell Biology, University of Iowa Carver College of Medicine, Iowa City, IA, USA.

Cell Reports
|February 14, 2019
PubMed
Summary

A new tool, PyMINEr, automates single-cell RNA sequencing (scRNA-seq) analysis for biologists. It identifies cell types, pathways, gene regulation, and signaling networks, aiding disease research.

Keywords:
BMPPyMINErWNTautocrine-paracrinecell type identificationcystic fibrosisnetworkspancreatic isletssingle-cell RNA-seqsystems biology

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Area of Science:

  • Bioinformatics
  • Genomics
  • Systems Biology

Background:

  • Limited user-friendly tools exist for in-depth single-cell RNA sequencing (scRNA-seq) data analysis by biologists.
  • Complex informatics pipelines hinder the accessibility of scRNA-seq data for biological interpretation.

Purpose of the Study:

  • To introduce PyMINEr, an automated informatics tool for comprehensive scRNA-seq analysis.
  • To demonstrate PyMINEr's utility in identifying cell types, pathways, gene regulatory networks, and signaling networks.

Main Methods:

  • Development of the PyMINEr informatics framework.
  • Application of PyMINEr to human pancreatic islet scRNA-seq datasets.
  • Construction of consensus co-expression and signaling networks across multiple datasets.

Main Results:

  • PyMINEr automates cell type identification, pathway analysis, and network detection.
  • Analysis revealed concordance between scRNA-seq graph structure, protein-protein interactions, and 3D genomics.
  • The tool identified BMP-WNT signaling changes linked to cystic fibrosis.

Conclusions:

  • PyMINEr provides a valuable, automated framework for scRNA-seq analysis.
  • The tool facilitates the exploration of gene co-expression and signaling networks.
  • PyMINEr aids in understanding disease mechanisms and genetic variant associations.