scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data

• Published in: Nature communications 2024-08, Vol.15 (1), p.7561-20, Article 7561
• Main Authors: Xu, Yunpei, Wang, Shaokai, Feng, Qilong, Xia, Jiazhi, Li, Yaohang, Li, Hong-Dong, Wang, Jianxin
• Format: Article
• Language: English
• Subjects: 38/91; 631/114/2397; 631/1647/48; 631/208/199; 631/553/117; Algorithms; Animals; Annotations; Anomalies; Biological activity; Biological effects; Clear cell-type renal cell carcinoma; Cluster Analysis; Clustering; Computational Biology - methods; Datasets; Decomposition; Disease; Gene expression; Gene Expression Profiling - methods; Gene sequencing; Humanities and Social Sciences; Humans; Immune system; Immunology; Intestine; Mice; multidisciplinary; Pancreas - cytology; Pancreas - metabolism; Pancreas - pathology; Pancreatic carcinoma; Pathogenesis; Rare diseases; RNA-Seq - methods; Science; Science (multidisciplinary); Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; State-of-the-art reviews; Transcriptome; Transcriptomics
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-51891-9

Single-cell RNA sequencing (scRNA-seq) technologies have become essential tools for characterizing cellular landscapes within complex tissues. Large-scale single-cell transcriptomics holds great potential for identifying rare cell types critical to the pathogenesis of diseases and biological processes. Existing methods for identifying rare cell types often rely on one-time clustering using partial or global gene expression. However, these rare cell types may be overlooked during the clustering phase, posing challenges for their accurate identification. In this paper, we propose a Cluster decomposition-based Anomaly Detection method (scCAD), which iteratively decomposes clusters based on the most differential signals in each cluster to effectively separate rare cell types and achieve accurate identification. We benchmark scCAD on 25 real-world scRNA-seq datasets, demonstrating its superior performance compared to 10 state-of-the-art methods. In-depth case studies across diverse datasets, including mouse airway, brain, intestine, human pancreas, immunology data, and clear cell renal cell carcinoma, showcase scCAD’s efficiency in identifying rare cell types in complex biological scenarios. Furthermore, scCAD can correct the annotation of rare cell types and identify immune cell subtypes associated with disease, thereby offering valuable insights into disease progression. Identifying rare cells is essential for advancing our understanding of complex biological systems and disease mechanisms. Here, authors propose scCAD, a method that combines cluster decomposition and anomaly detection to effectively identify rare cell types across diverse biological scenarios.