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HiDeF: identifying persistent structures in multiscale 'omics data.

Fan Zheng1, She Zhang2, Christopher Churas3

  • 1Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA. f6zheng@health.ucsd.edu.

Genome Biology
|January 8, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces HiDeF, a novel method using persistent homology to find robust data structures across all scales. It enhances cell type discovery in transcriptomics and reveals WNT pathway hijacking in SARS-CoV-2 protein interactions.

Keywords:
Community detectionMultiscalePersistent homologyProtein-protein interaction networkResolutionSingle-cell clusteringSystems biology

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Area of Science:

  • Computational Biology
  • Mathematical Topology
  • Systems Biology

Background:

  • Omics studies' outcomes depend on analysis scale, impacting cell type identification and protein network analysis.
  • Current methods may overemphasize broad or specific structures, missing robust patterns at multiple scales.

Purpose of the Study:

  • To introduce HiDeF, a method applying persistent homology for robust, multi-scale data structure identification.
  • To improve the cataloging of cell types from single-cell transcriptomes.
  • To analyze protein interaction networks, specifically in the context of viral infections.

Main Methods:

  • Utilized persistent homology, a concept from mathematical topology.
  • Applied the HiDeF method to mouse single-cell transcriptomes.
  • Analyzed SARS-CoV-2 protein-protein interaction networks.

Main Results:

  • Significantly expanded the catalog of identified cell types in mouse single-cell transcriptomes.
  • Suggested SARS-CoV-2 hijacks the WNT signaling pathway based on protein interaction analysis.
  • Developed the HiDeF method, available in Python and Cytoscape.

Conclusions:

  • HiDeF offers a robust approach to identify biological structures across multiple scales.
  • The method has broad applicability in omics and network biology.
  • HiDeF facilitates deeper insights into cellular heterogeneity and molecular mechanisms.