A comparative study of manifold learning methods for scRNA-seq with a trajectory-aware metric
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DOI: 10.1038/s41598-025-14301-8
Abstract: Single-cell RNA sequencing (scRNA-seq) enables detailed analysis of cellular diversity, but the data’s high dimensionality presents analytical challenges. We compare four dimensionality reduction methods-PCA, t-SNE, UMAP, and Diffusion Maps-on three benchmark scRNA-seq datasets (PBMC3k, Pancreas,… read more here.
Keywords: study manifold; trajectory aware; comparative study; scrna seq ... See more keywords