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, and 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.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

“…For actual network inference, it uses a VBEM framework using variational calculus to optimize the network models’ marginal likelihood and posterior distributions. In a different method, HBFM (Hierarchical Bayesian Factor Model) uses a sparse hierarchical Bayesian factor model to formulate the impact of gene expression by various factors associated with each cell, and a gene regulatory network structure is constructed by examining the shared factors between pairs of genes [ 348 ].…”

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

“…Single-cell gene expression offers a granular view of active pathways in a cell type-specific manner and potentially allows for the construction of cell type-specific networks. In fact, the rapid advances of single-cell sequencing technology have already allowed network analysis methods to be applied directly to data from single-cell RNA-sequencing (scRNA-seq) ( Crow et al, 2016 ; Aibar et al, 2017 ; Chan et al, 2017 ; Fiers et al, 2018 ; Papili Gao et al, 2018 ; van Dijk et al, 2018 ; Lamere and Li, 2019 ; Jackson et al, 2020 ; Sekula et al, 2020 ; Ye et al, 2020 ) with integration of other data modalities for improved network inference ( Aibar et al, 2017 ; Chan et al, 2017 ; Papili Gao et al, 2018 ; van Dijk et al, 2018 ; Jackson et al, 2020 ; Pratapa et al, 2020 ). Furthermore, matching single-cell and bulk patient samples could provide an invaluable resource for single-cell driven network investigations that can be compared to and related back to bulk tissues.…”

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

“…Zand and Ruan ( 2020 ) have recently developed a gene co-expression network-based method called ‘netImpute’ to alleviate this dropout issue (figure 2 B). A similar effort was made using a Bayesian factor model (Sekula et al 2020 ). In the case of single-cell RNA-seq experiments where the dataset consists of several biologically distinct unknown sample groups, identifying differentially expressed clusters with similar expression patterns is challenging.…”