Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA.
Nat Methods. 2024 Aug;21(8):1466-1469. doi: 10.1038/s41592-024-02365-9. Epub 2024 Jul 25.
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.
在这里，我们提出了 biVI，它结合了 scVI 的变分自动编码器框架和描述 RNA 分子转录和剪接动力学的生物物理模型。我们在模拟和实验的单细胞 RNA 测序数据上证明，biVI 保留了变分自动编码器在低维空间中捕获细胞类型结构的能力，同时进一步能够全面探索系统爆发大小和降解率等生物物理机制，这些机制是观察结果的基础。
