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arXiv · 1902.08138

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

Cassandra Burdziak·Elham Azizi·Sandhya Prabhakaran·Dana Pe'er
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Abstract

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.

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Cassandra Burdziak, Elham Azizi, Sandhya Prabhakaran, Dana Pe'er. 2019-02-21. A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks. https://arxiv.org/abs/1902.08138

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