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  3. Context-aware Self-training Framework For Cell Type Annotation Using Marker Genes.

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Context-Aware Self-Training Framework for Cell Type Annotation Using Marker Genes.

Hegang Chen, Yuyin Lu, Yanghui Rao

    IEEE Transactions on Computational Biology and Bioinformatics
    |April 28, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    CSSTA enhances single-cell annotation by using marker gene context and improved self-training. This novel approach boosts accuracy and cell-cell association recognition for better single-cell data analysis.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Single-cell annotation is crucial for analyzing single-cell data.
    • Self-training strategies improve annotation accuracy but require tailored frameworks for single-cell applications.

    Purpose of the Study:

    • To develop a context-aware self-training model (CSSTA) for improved single-cell data annotation.
    • To enhance the compatibility of marker genes and optimize self-training strategies for single-cell data.

    Main Methods:

    • CSSTA incorporates contextual information of marker genes to generate high-quality pseudo-labels.
    • It employs distinct high- and low-confidence pseudo-label recognition and supervision strategies.
    • The model integrates cell-cell association insights from single-cell foundation models.

    Main Results:

    • Contextual marker gene information significantly improves cell-cell type association recognition.
    • CSSTA outperforms existing state-of-the-art methods in benchmark experiments.
    • The model demonstrates potential for hierarchical cellular annotation.

    Conclusions:

    • CSSTA offers a more effective self-training framework for single-cell annotation.
    • The integration of marker gene context and advanced pseudo-labeling strategies enhances model performance.
    • CSSTA shows promise for complex hierarchical annotation tasks in single-cell biology.