siVAE: interpretable deep generative models for single-cell transcriptomes
Abstract Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-02-01
|
| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-023-02850-y |