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scCTS:从群体水平的单细胞 RNA-seq 中识别细胞类型特异性标记基因。

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

机构信息

Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA.

School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-SZ), Shenzhen, 518172, Guangdong, China.

出版信息

Genome Biol. 2024 Oct 14;25(1):269. doi: 10.1186/s13059-024-03410-8.

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.

摘要

单细胞 RNA 测序 (scRNA-seq) 为复杂样本中的单个细胞提供基因表达谱,有助于检测细胞类型特异性标记基因。在具有多个供体的 scRNA-seq 实验中,群体水平的变异给细胞类型特异性基因检测带来了额外的复杂性,例如,它们可能不会出现在所有供体中。受此观察结果的启发,我们开发了一种名为 scCTS 的统计模型,用于从群体水平的 scRNA-seq 数据中识别细胞类型特异性基因。广泛的数据分析表明,与传统方法相比,该方法可以识别出更具生物学意义的细胞类型特异性基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db62/11472465/c952de705de5/13059_2024_3410_Fig1_HTML.jpg

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