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Table 2 Details of experimental results based on NMI scores for various dimension reduction algorithms, including the DR-A, PCA, ZIFA, scVI, SAUCIE, t-SNE, and UMAP methods. We carried out the experiments using the Rosenberg-156 k, Zheng-73 k, Zheng-68 k, Macosko-44 k, and Zeisel-3 k datasets. These dimension reduction algorithms were investigated with (a) 2 latent dimensions (K = 2), (b) 10 latent dimensions (K = 10), and (c) 20 latent dimensions (K = 20)

From: A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis

AlgorithmRosenberg-156 kZheng-73 kZheng-68 kMacosko-44 kZeisel-3 k
(a) K = 2
(b) K = 10
(c) K = 20
  1. N/A denotes that we could not run the given algorithm