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CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data

Author

Listed:
  • Simone Puccio

    (IRCCS Humanitas Research Hospital
    UoS Milan, National Research Council)

  • Giorgio Grillo

    (National Research Council)

  • Giorgia Alvisi

    (IRCCS Humanitas Research Hospital)

  • Caterina Scirgolea

    (IRCCS Humanitas Research Hospital)

  • Giovanni Galletti

    (IRCCS Humanitas Research Hospital
    University of California San Diego)

  • Emilia Maria Cristina Mazza

    (IRCCS Humanitas Research Hospital)

  • Arianna Consiglio

    (National Research Council)

  • Gabriele De Simone

    (Flow Cytometry Core, IRCCS Humanitas Research Hospital)

  • Flavio Licciulli

    (National Research Council)

  • Enrico Lugli

    (IRCCS Humanitas Research Hospital)

Abstract

Flow cytometry (FCM) can investigate dozens of parameters from millions of cells and hundreds of specimens in a short time and at a reasonable cost, but the amount of data that is generated is considerable. Computational approaches are useful to identify novel subpopulations and molecular biomarkers, but generally require deep expertize in bioinformatics and the use of different platforms. To overcome these limitations, we introduce CRUSTY, an interactive, user-friendly webtool incorporating the most popular algorithms for FCM data analysis, and capable of visualizing graphical and tabular results and automatically generating publication-quality figures within minutes. CRUSTY also hosts an interactive interface for the exploration of results in real time. Thus, CRUSTY enables a large number of users to mine complex datasets and reduce the time required for data exploration and interpretation. CRUSTY is accessible at https://crusty.humanitas.it/ .

Suggested Citation

  • Simone Puccio & Giorgio Grillo & Giorgia Alvisi & Caterina Scirgolea & Giovanni Galletti & Emilia Maria Cristina Mazza & Arianna Consiglio & Gabriele De Simone & Flavio Licciulli & Enrico Lugli, 2023. "CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40790-0
    DOI: 10.1038/s41467-023-40790-0
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    1. Sara Biasi & Lara Gibellini & Domenico Lo Tartaro & Simone Puccio & Claudio Rabacchi & Emilia M. C. Mazza & Jolanda Brummelman & Brandon Williams & Kelly Kaihara & Mattia Forcato & Silvio Bicciato & M, 2021. "Circulating mucosal-associated invariant T cells identify patients responding to anti-PD-1 therapy," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Shobana V. Stassen & Gwinky G. K. Yip & Kenneth K. Y. Wong & Joshua W. K. Ho & Kevin K. Tsia, 2021. "Generalized and scalable trajectory inference in single-cell omics data with VIA," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    3. Hao Chen & Mai Chan Lau & Michael Thomas Wong & Evan W Newell & Michael Poidinger & Jinmiao Chen, 2016. "Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-17, September.
    4. 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.
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