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VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics.

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We developed VEGA, a novel deep learning model for transcriptomics data analysis. VEGA enhances interpretability of variational autoencoders by linking latent variables to gene modules, offering biological insights.

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Area of Science:

  • Computational Biology
  • Genomics
  • Machine Learning

Background:

  • Deep learning, particularly variational autoencoders (VAEs), has advanced transcriptomics data analysis.
  • Current VAEs lack interpretability in their latent space, limiting biological insights.
  • Interpretable models are crucial for understanding complex biological systems.

Purpose of the Study:

  • Introduce VEGA (VAE Enhanced by Gene Annotations), a novel sparse VAE architecture.
  • Enable direct interpretability of latent variables by mirroring gene modules in the decoder.
  • Provide a biologically interpretable framework for transcriptomics data analysis.

Main Methods:

  • Developed a sparse VAE architecture (VEGA).
  • Integrated user-provided gene modules (pathways, gene regulatory networks, cell types) into the decoder.
  • Applied VEGA to diverse transcriptomics datasets.

Main Results:

  • VEGA successfully recapitulates cellular responses to treatments.
  • The model reveals the status of master regulators.
  • VEGA jointly identifies cell type and cellular state in developmental data.

Conclusions:

  • VEGA offers direct interpretability for VAE latent spaces in transcriptomics.
  • The approach serves as an explanatory biological model for development and drug response.
  • VEGA facilitates deeper understanding of gene function and biological processes.