A comparative study of manifold learning methods for scRNA-seq with a trajectory-aware metric

A comparative study of manifold learning methods for scRNA-seq with a trajectory-aware metric

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, Pan...

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Bibliographic Details
Main Authors: Mehdi Nadjafikhah, Mohammad Nasiri
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Single-cell RNA sequencing
Dimensionality reduction
Manifold learning
Trajectory-aware embedding score (TAES)
Pseudotime analysis
Cellular heterogeneity
Online Access:https://doi.org/10.1038/s41598-025-14301-8
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https://doi.org/10.1038/s41598-025-14301-8

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