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ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists

Author

Listed:
  • Raman Sethi

    (Matrix)

  • Kok Siong Ang

    (Proteos)

  • Mengwei Li

    (Proteos)

  • Yahui Long

    (Proteos)

  • Jingjing Ling

    (Immunos)

  • Jinmiao Chen

    (Matrix
    Proteos
    Level 3)

Abstract

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell’s streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application ( https://immunesinglecell.org/ezsc/ ) or a software package with a shinyApp interface ( https://github.com/JinmiaoChenLab/ezSingleCell2 ) for offline analysis.

Suggested Citation

  • Raman Sethi & Kok Siong Ang & Mengwei Li & Yahui Long & Jingjing Ling & Jinmiao Chen, 2024. "ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48188-2
    DOI: 10.1038/s41467-024-48188-2
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    References listed on IDEAS

    as
    1. Yahui Long & Kok Siong Ang & Mengwei Li & Kian Long Kelvin Chong & Raman Sethi & Chengwei Zhong & Hang Xu & Zhiwei Ong & Karishma Sachaphibulkij & Ao Chen & Li Zeng & Huazhu Fu & Min Wu & Lina Hsiu Ki, 2023. "Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    2. Anjali Rao & Dalia Barkley & Gustavo S. França & Itai Yanai, 2021. "Exploring tissue architecture using spatial transcriptomics," Nature, Nature, vol. 596(7871), pages 211-220, August.
    Full references (including those not matched with items on IDEAS)

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