Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2020.02.23.20026690: (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

    Software and Algorithms
    SentencesResources
    Healthy Control Data: The lung scRNA-seq data from the healthy controls was acquired from the Gene Expression Omnibus (GEO) database under the series number GSE122960 [12], which contains data of lung tissue from eight lung transplant donors generated using 3’ V2 chemistry kit on Chromium Single cell controller (10xGenomics).
    Gene Expression Omnibus
    suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)
    Specifically, splicing-aware aligner STAR [30] was used in FASTQs alignment.
    STAR
    suggested: (STAR, RRID:SCR_015899)
    Differential analysis for clusters: MAST [32] in Seurat v3 was used to perform differential analysis.
    MAST
    suggested: (MAST, RRID:SCR_016340)
    Single cell trajectory analysis: Slingshot [33] was used to perform pseudotime inference for the four myeloid cell groups.
    Slingshot
    suggested: (Slingshot, RRID:SCR_017012)
    Regulatory network inference: Single cell regulatory network for 4 myeloid groups was constructed with SCENIC [34].
    SCENIC
    suggested: (SCENIC, RRID:SCR_017247)
    ), KEGG pathway analyses and Gene Set Enrichment Analysis (GSEA) [35] were performed with clusterProfiler [36], which supports statistical analysis and visualization of functional profiles for genes and gene clusters.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    clusterProfiler
    suggested: (clusterProfiler, RRID:SCR_016884)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    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.

    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.