Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data
Yang Yang, Hongjian Sun, Yu Zhang, Tiefu Zhang, Jialei Gong, Yunbo Wei, Yong‐Gang Duan, Minglei Shu, Yuchen Yang, Di Wu, Di Yu
This study compares dimensionality reduction methods for bulk transcriptomic data, finding UMAP better preserves sample neighbourhood and clustering accuracy than PCA, MDS, and t-SNE.
Cell Reports · Jul 1, 2021Peer-reviewedBioinformatics