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Updated: May 17, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
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scMarkerGene: an interpretable neural network framework for cell-type-specific marker gene discovery.

Jingkai Zhang1,2,3, Si Hoi Kou1,2,3, Jiulu Zhao1,2,3,4

  • 1Center for Biomedical Digital Science, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, No. 190, Kaiyuan Avenue, Huangpu District, Guangzhou 510530, China.

Briefings in Bioinformatics
|May 15, 2026
PubMed
Summary

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scMarkerGene accurately identifies cell-type-specific marker genes in single-cell transcriptomics using an interpretable neural network. This framework improves upon existing methods by robustly capturing distinguishing features across diverse datasets.

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Accurate cell-type identification in single-cell transcriptomics is crucial for understanding cellular heterogeneity.
  • Existing marker gene discovery methods often suffer from noise, bias, and may identify highly expressed genes rather than truly specific ones.

Purpose of the Study:

  • To introduce scMarkerGene, an interpretable neural network framework for robust and accurate marker gene discovery in single-cell transcriptomics.
  • To provide a quantitative measure of gene influence on cell-type discrimination.

Main Methods:

  • Developed scMarkerGene, a neural network framework that generates a Contribution Score (CS) matrix.
  • Implemented a downstream specificity filtering process to refine gene identification.
Keywords:
artificial intelligencecell identitymarker gene discoverysingle cell data analysis

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  • Validated the framework on diverse single-cell RNA sequencing (scRNA-seq) datasets, spatial transcriptomics, and pseudotime data.

    • Main Results:

    • scMarkerGene demonstrates robustness to noise, varying cell numbers, annotation resolutions, and sequencing technologies.
    • The framework effectively identified cell-type-distinguishing genes, dynamic markers, and spatially resolved markers.
    • scMarkerGene transforms neural network predictions into interpretable gene-level importance signals.

    Conclusions:

    • scMarkerGene offers an efficient, accurate, and interpretable solution for marker gene discovery in single-cell analysis.
    • The framework shows broad potential for multi-omics data integration and interpretation.
    • The approach successfully identified marker genes across various transcriptomic data types and species.