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

ABSTRACT: 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 accura

SUBMITTER: Xu Y 

PROVIDER: S-EPMC11364754 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature