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Multi-batch single-cell comparative atlas construction by deep learning disentanglement

Author

Listed:
  • Allen W. Lynch

    (Harvard Medical School
    Dana-Farber Cancer Institute)

  • Myles Brown

    (Dana-Farber Cancer Institute
    Brigham and Women’s Hospital, and Harvard Medical School)

  • Clifford A. Meyer

    (Dana-Farber Cancer Institute
    Harvard T.H. Chan School of Public Health)

Abstract

Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL’s capacity for batch-confounded cell type discovery when applied to simulated datasets and embryonic development atlases with gene knockouts. CODAL improves the representation of RNA-seq and ATAC-seq modalities, yields interpretable modules of biological variation, and enables the generalization of other count-based generative models to multi-batched data.

Suggested Citation

  • Allen W. Lynch & Myles Brown & Clifford A. Meyer, 2023. "Multi-batch single-cell comparative atlas construction by deep learning disentanglement," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39494-2
    DOI: 10.1038/s41467-023-39494-2
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