Mapping the gene space at single-cell resolution with gene signal pattern analysis

Abstract

In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to map or create embeddings of the gene space. Here we formulate the gene embedding problem, design tasks with simulated single-cell data to evaluate representations, and establish ten relevant baselines. We then present a graph signal processing approach, called gene signal pattern analysis (GSPA), that learns rich gene representations from single-cell data using a dictionary of diffusion wavelets on the cell–cell graph. GSPA enables characterization of genes based on their patterning and localization on the cellular manifold. We motivate and demonstrate the efficacy of GSPA as a framework for diverse biological tasks, such as capturing gene co-expression modules, condition-specific enrichment and perturbation-specific gene–gene interactions. Then we showcase the broad utility of gene representations derived from GSPA, including for cell–cell communication (GSPA-LR), spatial transcriptomics (GSPA-multimodal) and patient response (GSPA-Pt) analysis. This work presents a graph signal processing method, gene signal pattern analysis, to embed gene signals from single-cell sequencing data. In diverse experimental set-ups and case studies, GSPA establishes a gene-based framework for single-cell analysis.