# A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks

arXiv: https://arxiv.org/abs/1902.08138 · HTML: https://mlsanity.com/paper/1902.08138/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2019.

## Built on by (most cited first, 1 of 1)

- [Deep and Probabilistic Models for Gene Regulatory Network Inference](https://mlsanity.com/paper/2607.16053/) (2026)

## Abstract (arXiv)

We present a Bayesian hierarchical multi-view mixture model termed Symphony that simultaneously learns clusters of cells representing cell types and their underlying gene regulatory networks by integrating data from two views: single-cell gene expression data and paired epigenetic data, which is informative of gene-gene interactions. This model improves interpretation of clusters as cell types with similar expression patterns as well as regulatory networks driving expression, by explaining gene-gene covariances with the biological machinery regulating gene expression. We show the theoretical advantages of the multi-view learning approach and present a Variational EM inference procedure. We demonstrate superior performance on both synthetic data and real genomic data with subtypes of peripheral blood cells compared to other methods.