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

Yang Yang | Zendy; Hongjian Sun | Zendy; Yu Zhang | Zendy; Tiefu Zhang | Zendy; Jialei Gong | Zendy; Yunbo Wei | Zendy; YongGang Duan | Zendy; Minglei Shu | Zendy; Yuchen Yang | Zendy; Di Wu | Zendy; Di Yu | Zendy

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

Author(s) -

Yang Yang,

Hongjian Sun,

Yu Zhang,

Tiefu Zhang,

Jialei Gong,

Yunbo Wei,

YongGang Duan,

Minglei Shu,

Yuchen Yang,

Di Wu,

Di Yu

Publication year - 2021

Publication title -

cell reports

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 6.264

H-Index - 154

eISSN - 2639-1856

pISSN - 2211-1247

DOI - 10.1016/j.celrep.2021.109442

Subject(s) - principal component analysis , dimensionality reduction , computer science , sample (material) , multidimensional scaling , projection (relational algebra) , nonlinear dimensionality reduction , random projection , transcriptome , curse of dimensionality , sample size determination , data mining , pattern recognition (psychology) , computational biology , artificial intelligence , mathematics , statistics , biology , algorithm , genetics , machine learning , chemistry , gene expression , gene , chromatography

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