Unsupervised clustering analysis of SARS-Cov-2 population structure reveals six major subtypes at early stage across the world

  1. Yawei Li
  2. Qingyun Liu
  3. Zexian Zeng
  4. Yuan Luo

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Abstract

Identifying the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16,873 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses.

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  1. Version published to 10.1101/2020.09.04.283358v4 on bioRxiv
  2. Version published to 10.1101/2020.09.04.283358v3 on bioRxiv
  3. Version published to 10.1101/2020.09.04.283358v2 on bioRxiv
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    SciScore for 10.1101/2020.09.04.283358: (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
    Multiple sequence alignments and pairwise alignments were constructed using CLUSTALW 2.1 (21).
    CLUSTALW
    suggested: (ClustalW, RRID:SCR_017277)
    We used substitutions as features to reconstruct the phylogenetic tree using FastTree 2 (22).
    FastTree
    suggested: (FastTree, RRID:SCR_015501)
    The phylogeny is rooted following Nextstrain pipeline using FigTree v1.4.4 (23).
    FigTree
    suggested: (FigTree, RRID:SCR_008515)
    Other figures and statistical analyses were generated by the ggplot2 library in R 3.6.1, the seaborn package in Python 3.7.6 and GraphPad Prism 8.0.2.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    The models were implemented using the Python package sklearn with the KMeans function, AgglomerativeClustering function and Birch function, respectively.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Inferring positive/purifying selection of individual sites: To test which position was under selective pressure, we used a set of programs available in HyPhy (28) to calculate nonsynonymous (dN) and synonymous (dS) substitution rates on a per-site basis to infer pervasive selection.
    HyPhy
    suggested: (HyPhy, RRID:SCR_016162)

    Results from OddPub: Thank you for sharing your 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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

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  5. Version published to 10.1101/2020.09.04.283358v1 on bioRxiv