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Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model.

Haoxiang Gao1, Kui Hua1, Xinze Wu1

  • 1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.

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|August 12, 2024
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Summary
This summary is machine-generated.

UniCoord creates a learnable coordinate system for single-cell transcriptomics. This model captures cell heterogeneity and aids in data augmentation and analysis of new cell types.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell transcriptomic data presents complex heterogeneities across multiple resolutions.
  • Developing a universal coordinate system is crucial for large-scale cell atlases and molecular studies.
  • Existing methods struggle to capture multifaceted cellular variations within a fixed system.

Purpose of the Study:

  • To develop a learnable universal coordinate model for single-cell transcriptomic data.
  • To capture major cellular heterogeneities and serve as a controlled generative model.
  • To enable enhanced data augmentation and analysis of cell atlases.

Main Methods:

  • Developed UniCoord, a joint variational autoencoder (VAE) model.
  • Represented single-cell transcriptomic data in a low-dimensional, interpretable latent space.
  • Configured latent dimensions for discrete/continuous features, supervised/unsupervised learning.

Main Results:

  • UniCoord effectively represents single-cell transcriptomic data in a lower-dimensional latent space.
  • Latent dimensions capture interpretable features, allowing generation of pseudo-transcriptomic profiles.
  • The model demonstrates utility as a pre-trained tool for analyzing unseen cell types.

Conclusions:

  • UniCoord offers a novel framework for a learnable universal coordinate system in single-cell biology.
  • The model facilitates improved analysis, comparison, and generation of cells with complex functions.
  • UniCoord provides a foundation for constructing comprehensive and dynamic cell atlases.