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Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Author

Listed:
  • Lyla Atta

    (Johns Hopkins University
    Johns Hopkins University
    Johns Hopkins University School of Medicine)

  • Jean Fan

    (Johns Hopkins University
    Johns Hopkins University
    Johns Hopkins University)

Abstract

Spatially resolved transcriptomic data demand new computational analysis methods to derive biological insights. Here, we comment on these associated computational challenges as well as highlight the opportunities for standardized benchmarking metrics and data-sharing infrastructure in spurring innovation moving forward.

Suggested Citation

  • Lyla Atta & Jean Fan, 2021. "Computational challenges and opportunities in spatially resolved transcriptomic data analysis," Nature Communications, Nature, vol. 12(1), pages 1-5, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25557-9
    DOI: 10.1038/s41467-021-25557-9
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    Cited by:

    1. Quentin Blampey & Kevin Mulder & Margaux Gardet & Stergios Christodoulidis & Charles-Antoine Dutertre & Fabrice André & Florent Ginhoux & Paul-Henry Cournède, 2024. "Sopa: a technology-invariant pipeline for analyses of image-based spatial omics," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Xin Yuan & Yanran Ma & Ruitian Gao & Shuya Cui & Yifan Wang & Botao Fa & Shiyang Ma & Ting Wei & Shuangge Ma & Zhangsheng Yu, 2024. "HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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