Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections

  1. Simone A. Thair
  2. Yudong D. He
  3. Yehudit Hasin-Brumshtein
  4. Suraj Sakaram
  5. Rushika Pandya
  6. Jiaying Toh
  7. David Rawling
  8. Melissa Remmel
  9. Sabrina Coyle
  10. George N. Dalekos
  11. Ioannis Koutsodimitropoulos
  12. Glykeria Vlachogianni
  13. Eleni Gkeka
  14. Eleni Karakike
  15. Georgia Damoraki
  16. Nikolaos Antonakos
  17. Purvesh Khatri
  18. Evangelos J. Giamarellos-Bourboulis
  19. Timothy E. Sweeney

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Abstract

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  1. Version published to 10.1016/j.isci.2020.101947
  2. ScreenIT

    SciScore for 10.1101/2020.06.18.20131326: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has some limitations due to the design of using public data for non-COVID-19 comparison. First, due to the limited nature of clinical studies in a pandemic, we had just 62 patients with COVID-19 compared to >650 with other viral infections, creating class imbalance in their comparison. Second, we did not investigate effects of severity on host response as this was mostly unavailable. It is possible that differences in severity between this COVID-19 cohort and the other viral cohorts was a confounder in our analysis. Third, we analysed differential expression at single pre-set significance and effect size thresholds. Choosing different thresholds (e.g., thresholds based on 80% statistical power in each analysis) would have identified different sets of differentially expressed genes. We provide ES and FDR values for all genes (Supplementary Table 2) to enable re-analysis of these genes based on thresholds that others may deem more appropriate. Supplementary Figure 4 is also provided to show the GO term enrichment results by varying cut-offs.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.06.18.20131326v1 on medRxiv