Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition

This article has been Reviewed by the following groups

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Abstract

No abstract available

Article activity feed

  1. SciScore for 10.1101/2022.02.16.480703: (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

    Antibodies
    SentencesResources
    To test for the association of factor 2 with the co-occurrence of lupus nephritis and anti-dsDNA autoantibodies, we first removed any donor scores for donors that did not have anti-dsDNA autoantibodies present.
    anti-dsDNA
    suggested: None
    Software and Algorithms
    SentencesResources
    Then, the trimmed-mean of M values (TMM) method in edgeR (Robinson et al., 2010) is used to adjust library sizes of the pseudobulked counts and the data are normalized and log-transformed.
    edgeR
    suggested: (edgeR, RRID:SCR_012802)
    When the CellChat database listed multiple receptor components as required for a specific LR interaction, we required all components of the receptor complex to be expressed.
    CellChat
    suggested: (CellChat, RRID:SCR_021946)
    For the B_m1 enrichment tests, we included gene sets from GOBP, KEGG, and Reactome databases.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    Reactome
    suggested: (Reactome, RRID:SCR_003485)
    GC content from each gene was retrieved using the EDASeq package in R (Risso et al., 2011).
    EDASeq
    suggested: (EDASeq, RRID:SCR_006751)

    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 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: Please consider improving the rainbow (“jet”) colormap(s) used on page 22. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.


    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.

    Results from scite Reference Check: We found no unreliable references.


    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.