Gao Qi, Ji Zhicheng, Wang Liuyang, Owzar Kouros, Li Qi-Jing, Chan Cliburn, Xie Jichun
bioRxiv. 2024 Apr 6:2023.05.24.541352. doi: 10.1101/2023.05.24.541352.
SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.
SifiNet是一个强大且准确的计算流程,用于识别不同的基因集、提取和注释细胞亚群,并阐明这些亚群之间的内在关系。独特的是,SifiNet绕过了通常集成到其他细胞注释流程中的细胞聚类阶段,从而避免了聚类中可能影响后续分析的潜在不准确之处。因此,与其他最先进的方法相比,SifiNet在多个实验数据集中表现出卓越的性能。SifiNet可以分析单细胞RNA和ATAC测序数据,从而生成全面的多组学细胞图谱。它作为一个开源的R包方便获取。