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Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies
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Deep generative models in single-cell omics.

Inés Rivero-Garcia1, Miguel Torres2, Fátima Sánchez-Cabo2

  • 1Universidad Politécnica de Madrid, Madrid, 28040, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain.

Computers in Biology and Medicine
|May 15, 2024
PubMed
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Deep Generative Models (DGMs) enable probability distribution inference in biomedical research, especially with scarce data. Single-cell omics advances allow DGMs for complex data integration and analysis, advancing health and disease understanding.

Area of Science:

  • Biomedical research
  • Computational biology
  • Genomics

Background:

  • Deep Generative Models (DGMs) are crucial for inferring complex probability distributions.
  • Biomedical research often faces challenges with scarce data for inference.
  • Single-cell omics technologies have generated large datasets, enabling advanced computational methods.

Purpose of the Study:

  • To highlight the utility of Deep Generative Models (DGMs) in biomedical research.
  • To discuss the application of DGMs in the context of single-cell omics data.
  • To emphasize the importance of cautious application and validation of DGMs in research.

Main Methods:

  • Application of DGMs for probability distribution inference.
  • Integration of multi-omics data from single-cell atlases.
Keywords:
Data integrationDeep generative modelsDiffusion modelGenerative adversarial networkMulti-omicsVariational autoencoderscATAC-seqscRNA-seq

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  • Utilizing DGMs for dimensionality reduction, cell type annotation, and RNA velocity inference.

    • Main Results:

    • DGMs address the challenge of inference in scarce biomedical data scenarios.
    • Single-cell omics data enables the application of DGMs to large-scale datasets.
    • DGMs are effective in various single-cell analysis tasks.

    Conclusions:

    • DGMs offer a powerful tool for integrative analysis of single-cell omics data.
    • Careful consideration of research questions and validation metrics is essential for DGMs.
    • DGMs hold significant promise for advancing the understanding of health and disease.