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