DeepAI AI Chat
Log In Sign Up

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

by   Cassandra Burdziak, et al.

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.


page 7

page 14

page 16


Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics

A key problem in computational biology is discovering the gene expressio...

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological model...

Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process

Single-cell RNA sequencing (scRNA-seq) is powerful technology that allow...

Identification of Biomarkers Controlling Cell Fate In Blood Cell Development

A blood cell lineage consists of several consecutive developmental stage...

Bayesian Optimization for Synthetic Gene Design

We address the problem of synthetic gene design using Bayesian optimizat...

A deep generative model for gene expression profiles from single-cell RNA sequencing

We propose a probabilistic model for interpreting gene expression levels...