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cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

Author

Listed:
  • Christina Bligaard Pedersen

    (Technical University of Denmark
    Rigshospitalet—Copenhagen University Hospital)

  • Søren Helweg Dam

    (Technical University of Denmark)

  • Mike Bogetofte Barnkob

    (University of Southern Denmark)

  • Michael D. Leipold

    (Stanford University School of Medicine)

  • Noelia Purroy

    (Dana-Farber Cancer Institute
    AstraZeneca)

  • Laura Z. Rassenti

    (University of California, San Diego)

  • Thomas J. Kipps

    (University of California, San Diego)

  • Jennifer Nguyen

    (Harvard Medical School)

  • James Arthur Lederer

    (Harvard Medical School)

  • Satyen Harish Gohil

    (Dana-Farber Cancer Institute
    University College London
    University College London Hospitals NHS Trust)

  • Catherine J. Wu

    (Dana-Farber Cancer Institute
    Broad Institute of MIT and Harvard)

  • Lars Rønn Olsen

    (Technical University of Denmark)

Abstract

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.

Suggested Citation

  • Christina Bligaard Pedersen & Søren Helweg Dam & Mike Bogetofte Barnkob & Michael D. Leipold & Noelia Purroy & Laura Z. Rassenti & Thomas J. Kipps & Jennifer Nguyen & James Arthur Lederer & Satyen Har, 2022. "cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29383-5
    DOI: 10.1038/s41467-022-29383-5
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    1. Erwin M. Schoof & Benjamin Furtwängler & Nil Üresin & Nicolas Rapin & Simonas Savickas & Coline Gentil & Eric Lechman & Ulrich auf dem Keller & John E. Dick & Bo T. Porse, 2021. "Quantitative single-cell proteomics as a tool to characterize cellular hierarchies," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
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    2. David G. Priest & Takeshi Ebihara & Janyerkye Tulyeu & Jonas N. Søndergaard & Shuhei Sakakibara & Fuminori Sugihara & Shunichiro Nakao & Yuki Togami & Jumpei Yoshimura & Hiroshi Ito & Shinya Onishi & , 2024. "Atypical and non-classical CD45RBlo memory B cells are the majority of circulating SARS-CoV-2 specific B cells following mRNA vaccination or COVID-19," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    3. Mikhael D. Manurung & Friederike Sonnet & Marie-Astrid Hoogerwerf & Jacqueline J. Janse & Yvonne Kruize & Laura de Bes-Roeleveld & Marion König & Alex Loukas & Benjamin G. Dewals & Taniawati Supali & , 2024. "Controlled human hookworm infection remodels plasmacytoid dendritic cells and regulatory T cells towards profiles seen in natural infections in endemic areas," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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