@inproceedings{
weinberger2023a,
title={A deep generative model of single-cell methylomic data},
author={Ethan Weinberger and Su-In Lee},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=Mg2DM0F3AY}
}
Single-cell DNA methyolme profiling platforms based on bisulfite sequencing techniques promise to enable the exploration of epigenomic heterogeneity at an unprecedented resolution. However, substantial noise resulting from technical limitations of these platforms can impede downstream analyses of the data. Here we present methylVI, a deep generative model that learns probabilistic representations of single-cell methylation data which explicitly account for the unique characeteristics of bisulfite-sequencing-derived methylomic data.  After initially validating the quality of our model's fit, we proceed to demonstrate how methylVI can facilitate common downstream analysis tasks, including integrating data collected using different sequencing platforms and producing denoised methylome profiles. Our implementation of methylVI is publicly available at https://github.com/suinleelab/methylVI.
A deep generative model of single-cell methylomic data
Ethan Weinberger Su-In Lee