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.
All Keywords
【저자키워드】 Systems biology, community detection, Protein-protein interaction network, Persistent homology, Resolution, Multiscale, Single-cell clustering, 【초록키워드】 Structure, outcome, Protein, Clustering, Community, protein interaction, application, Analysis, Cytoscape, cell types, homology, Topology, single-cell transcriptomes, Affect, Cell, robust, identify, significantly, expand, mathematical, single-cell transcriptome, 【제목키워드】 Structure,
【저자키워드】 Systems biology, community detection, Protein-protein interaction network, Persistent homology, Resolution, Multiscale, Single-cell clustering, 【초록키워드】 Structure, outcome, Protein, Clustering, Community, protein interaction, application, Analysis, Cytoscape, cell types, homology, Topology, single-cell transcriptomes, Affect, Cell, robust, identify, significantly, expand, mathematical, single-cell transcriptome, 【제목키워드】 Structure,