Integrated analysis of multimodal single-cell data

  1. Yuhan Hao
  2. Stephanie Hao
  3. Erica Andersen-Nissen
  4. William M. Mauck
  5. Shiwei Zheng
  6. Andrew Butler
  7. Maddie J. Lee
  8. Aaron J. Wilk
  9. Charlotte Darby
  10. Michael Zagar
  11. Paul Hoffman
  12. Marlon Stoeckius
  13. Efthymia Papalexi
  14. Eleni P. Mimitou
  15. Jaison Jain
  16. Avi Srivastava
  17. Tim Stuart
  18. Lamar B. Fleming
  19. Bertrand Yeung
  20. Angela J. Rogers
  21. Juliana M. McElrath
  22. Catherine A. Blish
  23. Raphael Gottardo
  24. Peter Smibert
  25. Rahul Satija

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Abstract

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce ‘weighted-nearest neighbor’ analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity.

Availability

Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

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    SciScore for 10.1101/2020.10.12.335331: (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 code.

    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.

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  2. Version published to 10.1101/2020.10.12.335331 on bioRxiv