Skip to main content
Fig. 3 | BioData Mining

Fig. 3

From: ScInfoVAE: interpretable dimensional reduction of single cell transcription data with variational autoencoders and extended mutual information regularization

Fig. 3

Visualization of identified clusters. The partitions identified with scDeepCluster, scziDesk, scGNN, ScInfoVAE(RSC) and ScInfoVAE(KM)) on five datasets (Muraro, Quake Limb Muscle, Quake Smart seq2 Trachea ,Young, Adam). The plots illustrate the t-SNE 2D projections of the created embeddings. All selected methods start by producing an embedding for the cells, which is clustered in a second phase. The quality of the method depends on both the created embedding and the clustering algorithm. Both our methods clustered the same embedding, produced by ScInfoVAE

Back to article page

BioData Mining

ISSN: 1756-0381