HiDeF: identifying persistent structures in multiscale ‘omics data

  1. Fan Zheng
  2. She Zhang
  3. Christopher Churas
  4. Dexter Pratt
  5. Ivet Bahar
  6. Trey Ideker

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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.

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  1. Version published to 10.1186/s13059-020-02228-4
  2. Version published to 10.1101/2020.06.16.151555v3 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.16.151555: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To determine we use the extended Louvain algorithm implemented in the Python package louvain-igraph (http://github.com/vtraag/louvain-igraph; version 0.6.1).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Conos/Walktrap uses a score based on the gain of modularity in merging two communities, whereas TooManyCells uses the modularity of each binary partition.
    Conos/Walktrap
    suggested: None
    This collection contained two versions of the STRING interaction database, with the second removing edges from text mining (labeled STRING-t versus STRING, respectively; Fig. 3).
    STRING
    suggested: (STRING, RRID:SCR_005223)
    This list was expanded to include additional human proteins connected to two or more of the 332 virus-interacting human proteins in the new BioPlex 3.0 network [36].
    BioPlex
    suggested: (BioPlex, RRID:SCR_016144)
    HiDeF was applied to this network with the same parameter settings as for other protein-protein interaction networks (see previous Methods sections), and enrichment analysis was performed via g:Profiler [54] (Fig. 3f,g).
    g:Profiler
    suggested: (G:Profiler, RRID:SCR_006809)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  4. Version published to 10.1101/2020.06.16.151555v2 on bioRxiv
  5. Version published to 10.1101/2020.06.16.151555v1 on bioRxiv