IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-31104-x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-31104-x
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-31104-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yichuan Cao & Xiamiao Zhao & Songming Tang & Qun Jiang & Sijie Li & Siyu Li & Shengquan Chen, 2024. "scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. John Arevalo & Ellen Su & Jessica D. Ewald & Robert Dijk & Anne E. Carpenter & Shantanu Singh, 2024. "Evaluating batch correction methods for image-based cell profiling," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Xin Tang & Jiawei Zhang & Yichun He & Xinhe Zhang & Zuwan Lin & Sebastian Partarrieu & Emma Bou Hanna & Zhaolin Ren & Hao Shen & Yuhong Yang & Xiao Wang & Na Li & Jie Ding & Jia Liu, 2023. "Explainable multi-task learning for multi-modality biological data analysis," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    4. Adityanarayanan Radhakrishnan & Sam F. Friedman & Shaan Khurshid & Kenney Ng & Puneet Batra & Steven A. Lubitz & Anthony A. Philippakis & Caroline Uhler, 2023. "Cross-modal autoencoder framework learns holistic representations of cardiovascular state," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Kai Cao & Qiyu Gong & Yiguang Hong & Lin Wan, 2022. "A unified computational framework for single-cell data integration with optimal transport," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    6. Caitlin E. Carey & Rebecca Shafee & Robbee Wedow & Amanda Elliott & Duncan S. Palmer & John Compitello & Masahiro Kanai & Liam Abbott & Patrick Schultz & Konrad J. Karczewski & Samuel C. Bryant & Caro, 2024. "Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation," Nature Human Behaviour, Nature, vol. 8(8), pages 1599-1615, August.
    7. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31104-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.