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Reusable Single Cell for Iterative Epigenomic Analyses
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Semisupervised Generative Autoencoder for Single-Cell Data.

Trung Ngo Trong1, Juha Mehtonen2, Gerardo González2

  • 1University of Eastern Finland, School of Computing, Joensuu, Finland.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 4, 2019
PubMed
Summary

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Corrigendum to "Body condition score at calving, subclinical ketosis, postpartum body condition score losses, diseases, and fertility in Holstein cows: modelling confounding associations": [Veterinary and Animal Science, Volume 29, September 2025, 100493].

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This study introduces a SemI-SUpervised generative Autoencoder (SISUA) model. It leverages protein quantification (CITE-seq) to improve single-cell transcriptomics analysis for better cell phenotype discovery.

Area of Science:

  • Computational Biology
  • Genomics
  • Machine Learning

Background:

  • Single-cell transcriptomics reveals cellular heterogeneity by measuring mRNA abundance.
  • Integrating multi-modal data, like surface protein levels, can enhance single-cell analysis.
  • Current methods may not fully exploit complementary data sources.

Purpose of the Study:

  • To develop a novel Bayesian deep learning model for single-cell analysis.
  • To integrate CITE-seq protein quantification with gene expression data.
  • To improve the characterization of cell phenotypes using multi-modal single-cell data.

Main Methods:

  • Proposed a SemI-SUpervised generative Autoencoder (SISUA) model.
  • Utilized a deep variational autoencoder (VAE) neural network architecture.
Keywords:
autoencoderdeep learninggenerativeproteinsemisupervisedsingle-cellvariational

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  • Constrained the learning process using CITE-seq protein quantification data.

    • Main Results:

    • The SISUA model effectively integrates gene expression and protein data.
    • The generative approach enhances the learning process in single-cell analysis.
    • Demonstrated improved cell phenotype characterization through multi-modal data integration.

    Conclusions:

    • Bayesian deep learning offers a powerful framework for multi-modal single-cell data integration.
    • The SISUA model provides a robust method for leveraging CITE-seq data.
    • This approach advances the study of cellular diversity and function.