The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics

Abstract

Recent sequencing advances have enabled abundant multi-omics data generation for both single-cell and spatial contexts. Integrating such multimodal data is critical for decoding cellular and tissue-level complexity. However, compared with single-modality profiling, multimodal data often exhibit higher levels of noise, and existing methods typically overlook this challenge during integration. Meanwhile, most current approaches are tailored to either single-cell or spatial data, limiting their applicability across data types. Here we present DePass, a scalable graph learning framework for paired data integration in both single-cell and spatial multi-omics. We propose a coupled enhancement-integration architecture that iteratively denoises data and improves integrated embeddings. We systematically benchmarked DePass across 6 modalities, 9 tissue types and 13 experimental platforms, demonstrating superior integration accuracy. In the in-house colorectal cancer data, DePass further uncovered immune niche substructure and spatial tumour heterogeneity at near single-cell resolution. These results establish DePass as a unified and generalizable solution for multi-omics integration across diverse biological contexts. Li, Jiang and colleagues present DePass, a graph-based deep learning framework for integrating paired multi-omics data in spatial and single-cell contexts. A dual-enhancement approach is used to remove noise from raw omics data using neighbourhood aggregation.