Similarity-assisted variational autoencoder for nonlinear dimension reduction with application to single-cell RNA sequencing data.

ABSTRACT:

Background

Deep generative models naturally become nonlinear dimension reduction tools to visualize large-scale datasets such as single-cell RNA sequencing datasets for revealing latent grouping patterns or identifying outliers. The variational autoencoder (VAE) is a popular deep generative method equipped with encoder/decoder structures. The encoder and decoder are useful when a new sample is mapped to the latent space and a data point is generated from a point in a latent space. However, the VAE tends not to show grouping pattern clearly without additional annotation information. On the other hand, similarity-based dimension reduction methods such as t-SNE or UMAP present clear grouping patterns even though these methods do not have encoder/decoder structures.

Results

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SUBMITTER: Kim G 

PROVIDER: S-EPMC10647110 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature