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Fig. 2 | BMC Bioinformatics

Fig. 2

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

Fig. 2

2-D visualization for the Zeisel-3 k dataset. The Zeisel-3 k dataset was reduced to 2-D by using (a) DR-A, (b) PCA, (c) ZIFA, (d) scVI, (e) SAUCIE, (f) t-SNE, (g) UMAP, and (h) DR-A combined with t-SNE methods. Each point in the 2-D plot represents a cell in the testing set of the Zeisel dataset, which have 7 distinct cell types. There was an 80% training and 20% testing split from the original dataset in these experiments

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