Constructing Local Cell-Specific Networks from Single-Cell Data

• Activity Number: 183 - Genomics in Neuroscience
• Type: Invited
• Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
• Sponsor: ENAR
• Author(s): Kathryn Roeder* and Xuran Wang and David Choi
• Companies: CARNEGIE-MELLON UNIVERSITY and CARNEGIE-MELLON UNIVERSITY and Carnegie Mellon University
• Keywords: single cell RNA-seq; gene network; gene expression

Abstract

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 (CSN) for each single cell using a method inspired by Dai et al. (2019). The method is based on a non-parametric investigation of the joint distribution of gene expression, hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. The individual networks preserve information about the heterogeneity of the cells and having repeated estimates of network structure facilitates testing for difference in network structure between groups of cells. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we compared the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene co-expression changes.