Statistical challenges in the analysis of single-cell RNA-seq from brain cells

Thursday, March 18, 2021, 2:30-3:50pm, virtual access only

Kathryn Roeder
UPMC Professor of Statistics and Life Sciences in the Departments of Statistics and Data Science and Computational Biology
Carnegie Mellon University

Title: Statistical challenges in the analysis of single-cell RNA-seq from brain cells

Abstract: Quantification of gene expression using single cell RNA-sequencing of brain tissues, can be a critical step in the understanding of cell development and differences between cells sampled from case and control subjects.   We describe statistical challenges encountered analyzing expression of brain cells in the context of two projects. First, over-correction has been one of the main concerns in employing various data integration methods that risk removing the biological distinctions, which is harmful for cell type identification. Here, we present a simple yet surprisingly effective transfer learning model named cFIT for removing batch effects across experiments, technologies, subjects, and even species. Second, gene co-expression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level.  However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene co-expression networks.  We develop an alternative approach that estimates cell-specific networks for each single cell. We use this method to identify differential network genes in a comparison of cells from brains of individuals with autism spectrum disorder and those without.