scVAEDer: integrating deep diffusion models and variational autoencoders for single-cell transcriptomics analysis
Abstract Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensional representations, they have limitation...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-03-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-025-03519-4 |
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| Summary: | Abstract Discovering a lower-dimensional embedding of single-cell data can improve downstream analysis. The embedding should encapsulate both the high-level features and low-level variations. While existing generative models attempt to learn such low-dimensional representations, they have limitations. Here, we introduce scVAEDer, a scalable deep-learning model that combines the power of variational autoencoders and deep diffusion models to learn a meaningful representation that retains both global structure and local variations. Using the learned embeddings, scVAEDer can generate novel scRNA-seq data, predict perturbation response on various cell types, identify changes in gene expression during dedifferentiation, and detect master regulators in biological processes. |
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| ISSN: | 1474-760X |