Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Summary: Transcriptomic analysis plays a key role in biomedical research. Linear dimensionality reduction methods, especially principal-component analysis (PCA), are widely used in detecting sample-to-sample heterogeneity, while recently developed non-linear methods, such as t-distributed stochastic...

Full description

Bibliographic Details
Main Authors: Yang Yang, Hongjian Sun, Yu Zhang, Tiefu Zhang, Jialei Gong, Yunbo Wei, Yong-Gang Duan, Minglei Shu, Yuchen Yang, Di Wu, Di Yu
Format: Article
Language:English
Published: Elsevier 2021-07-01
Series:Cell Reports
Subjects:
bulk transcriptomics
dimensionality reduction
UMAP
t-SNE
PCA
clustering structure
Online Access:http://www.sciencedirect.com/science/article/pii/S2211124721008597

Similar Items

Similar Items