Speaker: Mike Sekula, PhD Candidate, Department of Bioinformatics and Biostatistics, University of Louisville

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

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. Here, we present a novel sparse Bayesian factor structure 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 the most 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. Results from simulation studies and real data analysis demonstrate the performance of our methodology in constructing GCNs.

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