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 t

SUBMITTER: Sekula M 

PROVIDER: S-EPMC7437941 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature