Identifying persistent structures in multiscale ‘omics data
Identifying persistent structures in multiscale ‘omics data
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.
Ideker Trey、Zheng Fan、Bahar Ivet、Zhang She、Churas Christopher、Pratt Dexter
Division of Genetics, Department of Medicine, University of CaliforniaDivision of Genetics, Department of Medicine, University of CaliforniaDepartment of Computational and Systems Biology, School of Medicine, University of PittsburghDepartment of Computational and Systems Biology, School of Medicine, University of PittsburghDivision of Genetics, Department of Medicine, University of CaliforniaDivision of Genetics, Department of Medicine, University of California
生物科学研究方法、生物科学研究技术生物科学现状、生物科学发展生物化学
systems biologymultiscalepersistent homologycommunity detectionresolutionsingle-cell clusteringprotein-protein interaction network
Ideker Trey,Zheng Fan,Bahar Ivet,Zhang She,Churas Christopher,Pratt Dexter.Identifying persistent structures in multiscale ‘omics data[EB/OL].(2025-03-28)[2025-05-10].https://www.biorxiv.org/content/10.1101/2020.06.16.151555.点此复制
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