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Title: Constructing local cell-specific networks from single-cell data Authors:  Xuran Wang - Icahn School of Medicine at Mount Sinai (United States) [presenting]
David Choi - Carnegie Mellon University (United States)
Kathryn Roeder - Carnegie Mellon University (United States)
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, the sparsity and heterogeneity of transcript count present challenges when constructing gene networks using traditional estimation techniques. These methods fail to detect complex co-expression patterns and obscure the heterogeneity across cell populations. We develop a method to estimate a cell-specific network (CSN) for every single cell that facilitates testing for differences in network structure between cell groups. Average CSNs provide stable estimates of network structure and detect gene block structure better than traditional measures. New downstream analysis methods using CSNs utilize more fully the information contained within them. We examined the evolution of gene networks in fetal brain cells and compared the CSNs of cells sampled from autism spectrum disorder and control subjects to reveal intriguing patterns in gene co-expression.