HiDeF: identifying persistent structures in multiscale 'omics data

Genome Biol. 2021 Jan 7;22(1):21. doi: 10.1186/s13059-020-02228-4.


In any 'omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here, we use the concept of persistent homology, drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.

Keywords: Community detection; Multiscale; Persistent homology; Protein-protein interaction network; Resolution; Single-cell clustering; Systems biology.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Animals
  • Computational Biology / methods*
  • Humans
  • Mice
  • SARS-CoV-2 / metabolism*
  • Viral Proteins / metabolism


  • Viral Proteins