ArticleBriefings in bioinformatics2024
CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- CORTADO: hill climbing optimization for cell-type specific marker gene discovery and clustering accuracy improvement.Bioinformatics advances · 2026Article
- UNICORN: Towards universal cellular expression prediction with a multi-task learning framework.Nature communications · 2025Article
- SuperGLUE facilitates an explainable training framework for multi-modal data analysis.Cell reports methods · 2025Article
- spRefine Denoises and Imputes Spatial Transcriptomics with a Reference-Free Framework Powered by Genomic Language Model.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
motivationSelecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and/or do not show cell-type-specific expression patterns to distinguish cell types.
resultsHere, we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https://github.com/VivLon/CosGeneGate/blob/main/marker gene list.xlsx.
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