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Updated: May 16, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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scALGSL: Active Learning and Graph Structure Learning for Cell Type Annotation From Single-Cell RNA-seq Data.

Zhi-Hua Du, Jia-Le Yi, Wei-Lin Hu

    IEEE Transactions on Computational Biology and Bioinformatics
    |May 14, 2026
    PubMed
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    scALGSL enhances single-cell RNA sequencing analysis by integrating active learning and graph optimization. This novel framework improves cell type annotation accuracy, crucial for understanding tissue heterogeneity and advancing precision medicine.

    Area of Science:

    • Computational Biology
    • Genomics
    • Bioinformatics

    Background:

    • Single-cell RNA sequencing (scRNA-seq) reveals tissue heterogeneity.
    • Accurate cell type annotation is essential for scRNA-seq data interpretation.
    • Current annotation methods face challenges like limited labeled data, poor graph structures, and lack of cell state information.

    Purpose of the Study:

    • To develop an innovative framework, scALGSL, for accurate cell type annotation in scRNA-seq data.
    • To address limitations of existing annotation methods, including label scarcity, suboptimal graph topology, and missing cell state information.

    Main Methods:

    • scALGSL integrates dynamic graph optimization with active learning.
    • A graph-guided active learning mechanism selects valuable training samples.

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  • A learnable graph structure optimization module refines adjacency matrices.
  • A cell state auxiliary pathway extracts functional features using pre-trained models.

    • Main Results:

    • scALGSL achieved an average accuracy of 0.896 and F1 score of 0.771 on a cancer dataset.
    • The framework demonstrated robustness in cross-platform tasks.
    • Integration of cell state information significantly improved performance.
    • Ablation studies confirmed the necessity of node selection and edge optimization modules.

    Conclusions:

    • scALGSL offers a scalable solution for precise cell annotation in scRNA-seq data.
    • This framework facilitates tumor microenvironment analysis and precision medicine applications.
    • The developed method effectively overcomes key challenges in current cell annotation techniques.