A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data

Abstract

Background: Gene co-expression networks (GCNs) are powerful tools that enable biologists to examine associations between genes during different biological processes. With the advancement of new technologies, such as single-cell RNA sequencing (scRNA-seq), there is a need for developing novel network methods appropriate for new types of data. Results: We present a novel sparse Bayesian factor model to explore the network structure associated with genes in scRNA-seq data. Latent factors impact the gene expression values for each cell and provide flexibility to account for common features of scRNA-seq: high proportions of zero values, increased cell-to-cell variability, overdispersion due to abnormally large expression counts. From our model, we construct a GCN by analyzing the positive and negative associations of the factors that are shared between each pair of genes. Conclusions: Simulation studies demonstrate that our methodology has high power in identifying gene-gene associations while maintaining a nominal false discovery rate. In real data analyses, our model identifies more known and predicted protein-protein interactions than other competing network models.