A robust multi-scale clustering framework for single-cell RNA-seq data analysis.

ABSTRACT: Recent advancements in single-cell RNA sequencing (scRNA-seq) technology have unlocked novel opportunities for deep exploration of gene expression patterns. However, the inherent high dimensionality, sparsity, and noise in scRNA-seq data pose significant challenges for existing clustering methods, especially in accurately identifying and classifying diverse cell types. To address these challenges, we introduce a new method, single-cell Multi-Scale Clustering Framework (scMSCF), which combines multi-dimensional PCA for dimensionality reduction, K-means clustering, and a weighted ensemble meta-clustering approach, enhanced by a self-attention-driven Transformer model to optimize clustering performance. scMSCF constructs an initial clustering framework using a multi-layer dimensionality reduc

SUBMITTER: Jiang S 

PROVIDER: S-EPMC12116994 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature