biVI: Biophysical modeling with variational autoencoders

We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, biVI models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that biVI captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions. On biological data, biVI provides a scalable route for identifying the biophysical mechanisms underlying gene expression. This analytical approach outlines a generalizable strateg for treating multimodal datasets generated by high-throughput, single-cell genomic assays.

Source code, examples and tutorials available. For more details, please see the preprint.

Installation:

pip3 install git+https://github.com/pachterlab/CGCCP_2023.git#subdirectory=BIVI