  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.
