SCVAEDER: INTEGRATING DEEP DIFFUSION MODELS AND VARIATIONAL AUTOENCODERS FOR SINGLE-CELL TRANSCRIPTOMICS ANALYSIS

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 limitations.Here, we introduce scVAEDer, a scalable deep-learn

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