SCENE: A Framework for Single-cell Gene Co-expression Network Analysis Across Cell Types and Conditions

Single-cell RNA sequencing (scRNA-seq) enables detailed characterization of gene expression across diverse cell types and conditions. While most analyses focus on differential expression and cell-type abundance, changes in gene–gene interaction patterns—key drivers of cellular function—remain underexplored at single-cell resolution. Because gene–gene correlations arise from coordinated activity within individual cells, co-expression network structure provides a natural representation of cellular state and its variation across conditions. Here, we present SCENE (Single-cell Co-Expression Network analysis), a workflow for identifying and comparing cell type–specific gene co-expression networks. Building on the idea that gene co-expression structures define cell states and may be cell type–specific, SCENE constructs donor- and cell type–specific gene co-expression networks from scRNA-seq data. These networks are compared across cell types or analyzed within a given cell type to assess associations between network changes and biological conditions. Differences in gene–gene interactions are summarized at both gene and pathway levels, enabling functional interpretation through integration with curated pathway and interaction resources such as MSigDB and STRING. We applied SCENE to the Chinese Immune Multi-Omics Atlas (CIMA), comprising peripheral blood mononuclear cells (PBMCs) from 421 healthy donors with diverse demographic profiles. Using this dataset, we investigate gene co-expression rewiring across immune cell types and its association with age and sex. Our analysis demonstrates the potential of SCENE to uncover pathway-level changes in gene interaction structure. SCENE complements existing single-cell analysis approaches by enabling systematic investigation of gene interaction dynamics. This framework provides new insights into the regulatory mechanisms underlying cellular heterogeneity and their modulation across biological conditions.