2019/02/21 by Cassandra Burdziak, Elham Azizi, Burdziak, Cassandra +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bayesian Methods and Mixture Models #FOS: Biological sciences #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Molecular Networks (q-bio.MN) #Quantitative Methods (q-bio.QM) #Single-cell and spatial transcriptomics
paper · pdf · doi:10.48550/arxiv.1902.08138
openalex publication_date 2019/02/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a Bayesian hierarchical multi-view mixture model termed Symphony\nthat simultaneously learns clusters of cells representing cell types and their\nunderlying gene regulatory networks by integrating data from two views:\nsingle-cell gene expression data and paired epigenetic data, which is\ninformative of gene-gene interactions. This model improves interpretation of\nclusters as cell types with similar expression patterns as well as regulatory\nnetworks driving expression, by explaining gene-gene covariances with the\nbiological machinery regulating gene expression. We show the theoretical\nadvantages of the multi-view learning approach and present a Variational EM\ninference procedure. We demonstrate superior performance on both synthetic data\nand real genomic data with subtypes of peripheral blood cells compared to other\nmethods.\n