SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data

  1. Sitara Persad
  2. Zi-Ning Choo
  3. Christine Dien
  4. Ignas Masilionis
  5. Ronan Chaligné
  6. Tal Nawy
  7. Chrysothemis C Brown
  8. Itsik Pe’er
  9. Manu Setty
  10. Dana Pe’er

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here, we present single-cell aggregation of cell-states (SEACells), an algorithm for identifying metacells; overcoming the sparsity of single-cell data, while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying accurate, compact, and well-separated metacells in both RNA and ATAC modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and measure gene accessibility in each metacell. Metacell-level analysis scales to large datasets and are particularly well suited for patient cohorts, including facilitation of data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation, and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a COVID-19 patient cohort.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2022.04.02.486748: (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
    After blocking, bone marrow cells were stained with CD3 Monoclonal Antibody (UCHT1), PE-Cyanine7, eBioscience™ (25-0038-42) 1:100 for 20 minutes at 4 °C.
    CD3
    suggested: (Thermo Fisher Scientific Cat# 25-0038-42, RRID:AB_1582253)
    UCHT1
    suggested: (Thermo Fisher Scientific Cat# 25-0038-42, RRID:AB_1582253)
    25-0038-42
    suggested: (Thermo Fisher Scientific Cat# 25-0038-42, RRID:AB_1582253)
    Experimental Models: Organisms/Strains
    SentencesResources
    Optimizing Archetypes and Cell Assignments: The objective function for kernel archetype analysis involves optimizing the non-convex product AB, and thus has many local minima.
    AB
    suggested: RRID:BDSC_203)
    Software and Algorithms
    SentencesResources
    SVD for scATAC-seq: We used the ArchR package23 for preprocessing of sc-ATAC data.
    ArchR
    suggested: (ArchR, RRID:SCR_020982)
    Peak calling: Peak calling was performed using ArchR23.
    ArchR23
    suggested: None
    SVD was used as input to cluster the data using phenograph and visualize using umaps.
    phenograph
    suggested: (Phenograph, RRID:SCR_016919)
    Metacell raw counts for different datasets were determined as described in the “Metacell identification” section.
    Metacell
    suggested: (Metacell, RRID:SCR_017013)
    We then computed a second aggregated gene expression using the single-cell groups from ATAC modality instead of the RNA modality.
    ATAC
    suggested: (Atac, RRID:SCR_015980)
    Metacells methods comparison: Baran et. al. MetaCell: MetaCell9 approach uses a non-parametric graph algorithm to partition scRNA-seq data into distinct metacells.
    MetaCell9
    suggested: None
    The “c7: immunologic signature” gene sets from Molecular Signature Database (MSigDB) (http://software.broadinstitute.org/gsea/msigdb/index.jsp) was used.
    http://software.broadinstitute.org/gsea/msigdb/index.jsp
    suggested: (Molecular Signatures Database, RRID:SCR_016863)

    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.

    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.

  2. Version published to 10.1101/2022.04.02.486748 on bioRxiv