2603.00364 Comparative Analysis of Dimensionality Reduction and Clustering Methods for Single-Cell RNA Sequencing Data
Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and transcriptomic landscapes. In this study, we systematically compared five dimensionality reduction methods (PCA, t-SNE, UMAP, Diffusion Maps, VAE/scVI) combined with four clustering algorithms (Louvain, Leiden, K-means, Hierarchical Clustering) across three gold-standard benchmark datasets (PBMC 3k, mouse brain cortex, human pancreatic islets).