Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data

Single-cell RNA-seq (scRNA-seq) can be used to characterize cellular heterogeneity in thousands of cells. The reconstruction of a gene network based on coexpression patterns is a fundamental task in scRNA-seq analyses, and the mutual exclusivity of gene expression can be critical to understand such heterogeneity. Here, we propose an approach for detecting communities from a gene network constructed on the basis of coexpression properties. The community-based comparison of multiple coexpression networks enables the identification of functionally related gene clusters that cannot be fully captured through differential gene expression-based analysis. We also developed a novel metric referred to as the exclusively expressed index (EEI) that identifies mutually exclusive gene pairs from sparse scRNA-seq data. EEI quantifies and ranks the exclusive expression levels of all gene pairs from binary expression patterns while maintaining robustness against a low sequencing depth. We applied our methods to glioblastoma scRNA-seq data and found that gene communities are partially conserved after serum stimulation despite a considerable number of differentially expressed genes. We also demonstrate that the identification of mutually exclusive gene sets with EEI can improve the sensitivity of capturing cellular heterogeneity. Our methods complement existing approaches and provide new biological insights from even a large sparse dataset in the single-cell analysis field. GSCs were cultured in Dulbecco’s modifiedEagle’s medium (DMEM)/F12 (Life Technologies) containing a B27 supplement minus vitamin A (Life Technologies), epidermal growth factor, and fibroblast growth factor 2 (20 ng/ml each; Wako Pure Chemicals Industries). For in vitro differentiation, GSCs were cultured in Dulbecco’s modified Eagle’s medium/F-12 medium (Life Technologies) containing 10% fetal bovine serum for the indicated times. Single-cell suspensions of GSCs or serum-induced differentiated GSCs were subjected to droplet-based scRNA-seq library preparation with the Chromium Single Cell 3' Reagent Kit v2 (10x Genomics), aiming for an estimated 2,000 cells per library and following the manufacturer’s instructions. The libraries were checked with a BioAnalyzer High Sensitivity Chip (Agilent), quantified with a KAPA Library Quantification kit (Roche), and then sequenced on the Illumina Hiseq 2500 platform in rapid mode.