CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis

Here, 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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Publication Details

Journal
Briefings in Bioinformatics
Published
2024-11-13
DOI
https://doi.org/10.1093/bib/bbae626
Citations
2
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.47

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article

CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis

Tianyu Liu, Le Zhang, Stephen M. Strittmatter, Chuan He et al.
2 citations
Briefings in Bioinformatics
Single-cell and spatial transcriptomics
0.47
article

CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis

Tianyu Liu, Le Zhang, Stephen M. Strittmatter, Chuan He, Wenxin Long, Hongyu Zhao, Yuge Wang, Zhiyuan Cao
article en
2 citations

Abstract

Here, 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.

Briefings in BioinformaticsVol. 26(1)
Pennsylvania State University (US), Yale University (US)
National Institutes of Health
Partnerships for the goals
Openalex Percentile: Top 34%
Single-cell and spatial transcriptomics
0.47
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