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

"Hurdle Model with Correlated Random Effects for Differential Expression of Single Cell RNA Sequencing Data"

Single cell RNA sequencing (scRNA-seq) technologies are revolutionary tools allowing researchers to examine gene expression at a single cell level. Traditionally, transcriptomic data have been analyzed from bulk samples, masking the heterogeneity now seen across individual cells.  Even within the same cellular population, genes can be highly expressed in some cells but not expressed (or lowly expressed) in others.  Therefore, the computational approaches used to analyze bulk RNA sequencing (RNA-seq) data are not appropriate for the analysis of scRNA-seq data.  In this talk, a novel statistical model is introduced for high dimensional and zero inflated scRNA-seq data to identify differentially expressed genes across cell types.  Additionally, the proposed model is compared to other popular methods designed for detecting differentially expressed genes in either bulk RNA-seq or scRNA-seq data.

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