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Decoding of persistent multiscale structures in complex biological networks.


ABSTRACT: Networks of genes, proteins, and cells exhibit significant multiscale organization, with distinct communities appearing at different spatial resolutions. Here, we apply the concept of 'persistent homology' to identify network communities that persist within defined scale ranges, yielding a hierarchy of robust structures in data. Application to mouse single-cell transcriptomes significantly expands the catalog of cell types identified by current tools, while analysis of SARS-COV-2 networks suggests pro-viral hijacking of WNT.

SUBMITTER: Zheng F 

PROVIDER: S-EPMC7310637 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Identifying persistent structures in multiscale 'omics data.

Zheng Fan F   Zhang She S   Churas Christopher C   Pratt Dexter D   Bahar Ivet I   Ideker Trey T  

bioRxiv : the preprint server for biology 20201003


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 tr  ...[more]

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