Song Won-Min, Ming Chen, Forst Christian V, Zhang Bin
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA.
Res Sq. 2024 Dec 23:rs.3.rs-5671748. doi: 10.21203/rs.3.rs-5671748/v1.
Cell clustering is an essential step in uncovering cellular architectures in single cell RNA-sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. Here, we develop a multi-scale clustering (MSC) approach to construct sparse cell-cell correlation network for identifying cell types and subtypes at multiscale resolution in an unsupervised manner. Based upon simulated, silver and gold standard data as well as real scRNA-seq data in diseases, MSC showed much improved performance in comparison to established benchmark methods, and identified biologically meaningful cell hierarchy to facilitate the discovery of novel disease associated cell subtypes and mechanisms.
细胞聚类是在单细胞RNA测序（scRNA-seq）数据中揭示细胞结构的关键步骤。然而，现有的细胞聚类方法在以更高分辨率剖析细胞景观的复杂结构方面设计不佳。在此，我们开发了一种多尺度聚类（MSC）方法，以构建稀疏的细胞-细胞相关网络，从而以无监督的方式在多尺度分辨率下识别细胞类型和亚型。基于模拟数据、银标准和金标准数据以及疾病中的真实scRNA-seq数据，与既定的基准方法相比，MSC表现出显著提高的性能，并识别出具有生物学意义的细胞层次结构，以促进发现与疾病相关的新型细胞亚型和机制。
