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Multi-domain translation between single-cell imaging and sequencing data using autoencoders

Author

Listed:
  • Karren Dai Yang

    (Massachusetts Institute of Technology)

  • Anastasiya Belyaeva

    (Massachusetts Institute of Technology)

  • Saradha Venkatachalapathy

    (National University of Singapore
    ETH Zurich and Paul Scherrer Institute)

  • Karthik Damodaran

    (National University of Singapore)

  • Abigail Katcoff

    (Massachusetts Institute of Technology)

  • Adityanarayanan Radhakrishnan

    (Massachusetts Institute of Technology)

  • G. V. Shivashankar

    (National University of Singapore
    ETH Zurich and Paul Scherrer Institute
    FIRC Institute of Molecular Oncology (IFOM))

  • Caroline Uhler

    (Massachusetts Institute of Technology)

Abstract

The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20249-2
    DOI: 10.1038/s41467-020-20249-2
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    Citations

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    Cited by:

    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. 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.
    3. 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.
    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. 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.
    7. 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.
    8. 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.

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