AI Article Synopsis
- biVI is a new method that merges variational autoencoders from scVI with biophysical models related to RNA transcription and splicing.
- It effectively analyzes single-cell RNA sequencing data by preserving the ability to identify cell types in a simplified form.
- Additionally, biVI allows for a deeper investigation of key biophysical processes, like burst sizes and degradation rates, that contribute to RNA behavior.
Article Abstract
Here we present biVI, which combines the variational autoencoder framework of scVI with biophysical models describing the transcription and splicing kinetics of RNA molecules. We demonstrate on simulated and experimental single-cell RNA sequencing data that biVI retains the variational autoencoder's ability to capture cell type structure in a low-dimensional space while further enabling genome-wide exploration of the biophysical mechanisms, such as system burst sizes and degradation rates, that underlie observations.
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| http://dx.doi.org/10.1038/s41592-024-02365-9 | DOI Listing |
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