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...

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Detalhes bibliográficos

Principais autores:	Yang Yang, Hongjian Sun, Yu Zhang, Tiefu Zhang, Jialei Gong, Yunbo Wei, Yong-Gang Duan, Minglei Shu, Yuchen Yang, Di Wu, Di Yu
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2021-07-01
coleção:	Cell Reports
Assuntos:	bulk transcriptomics dimensionality reduction UMAP t-SNE PCA clustering structure
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2211124721008597
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