scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq

Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.

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Publication Details

Journal
Genome biology
Published
2024-10-14
DOI
https://doi.org/10.1186/s13059-024-03410-8
Citations
4
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.94
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article

scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq

Luxiao Chen, Tao Deng, Hao Wu, Zhenxing Guo
4 citations
Genome biology
Single-cell and spatial transcriptomics
0.94
article

scCTS: identifying the cell type-specific marker genes from population-level single-cell RNA-seq

Luxiao Chen, Tao Deng, Hao Wu, Zhenxing Guo
article en
4 citations

Abstract

Single-cell RNA-sequencing (scRNA-seq) provides gene expression profiles of individual cells from complex samples, facilitating the detection of cell type-specific marker genes. In scRNA-seq experiments with multiple donors, the population level variation brings an extra layer of complexity in cell type-specific gene detection, for example, they may not appear in all donors. Motivated by this observation, we develop a statistical model named scCTS to identify cell type-specific genes from population-level scRNA-seq data. Extensive data analyses demonstrate that the proposed method identifies more biologically meaningful cell type-specific genes compared to traditional methods.

Genome biologyVol. 25(1)
Emory University (US), Shenzhen University (CN), Shenzhen Research Institute of Big Data (CN), Chinese University of Hong Kong, Shenzhen (CN), Shenzhen Institutes of Advanced Technology (CN), Shenzhen Technology University (CN)
Zero hunger
Openalex Percentile: Top 25%
Single-cell and spatial transcriptomics
0.94
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