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Diagonal integration of multimodal single-cell data: potential pitfalls and paths forward

Author

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  • Yang Xu

    (University of Tennessee)

  • Rachel Patton McCord

    (University of Tennessee)

Abstract

Diagonal integration of multimodal single-cell data emerges as a trending topic. However, empowering diagonal methods for novel biological discoveries requires bridging huge gaps. Here, we comment on potential risks and future directions of diagonal integration for multimodal single-cell data.

Suggested Citation

  • Yang Xu & Rachel Patton McCord, 2022. "Diagonal integration of multimodal single-cell data: potential pitfalls and paths forward," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31104-x
    DOI: 10.1038/s41467-022-31104-x
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    1. Karren Dai Yang & Anastasiya Belyaeva & Saradha Venkatachalapathy & Karthik Damodaran & Abigail Katcoff & Adityanarayanan Radhakrishnan & G. V. Shivashankar & Caroline Uhler, 2021. "Multi-domain translation between single-cell imaging and sequencing data using autoencoders," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. Zhana Duren & Wenhui Sophia Lu & Joseph G. Arthur & Preyas Shah & Jingxue Xin & Francesca Meschi & Miranda Lin Li & Corey M. Nemec & Yifeng Yin & Wing Hung Wong, 2021. "Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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    Cited by:

    1. Jules Samaran & Gabriel Peyré & Laura Cantini, 2024. "scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

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