HiDeF: identifying persistent structures in multiscale 'omics data.
Genome Biol
; 22(1): 21, 2021 01 07.
Article
in English
| MEDLINE | ID: covidwho-1015895
ABSTRACT
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
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Computational Biology
/
SARS-CoV-2
Type of study:
Prognostic study
Limits:
Animals
/
Humans
Language:
English
Journal:
Genome Biol
Journal subject:
Molecular Biology
/
Genetics
Year:
2021
Document Type:
Article
Affiliation country:
S13059-020-02228-4
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