Single-cell multi-omics and spatial multi-omics data integration via dual-path graph attention auto-encoder

Abstract

Single-cell multi-omics data integration enables joint analysis of the resolution at single-cell level to provide comprehensive and accurate understanding of complex biological systems, while spatial multi-omics data integration is benefit to the exploration of cell spatial heterogeneity to facilitate more diversified downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration on spatial information, and still have the room for performance improvement. A reliable multi-omics data integration method that can be applied to both single-cell and spatially resolved data is necessary and significant. We propose a single-cell multi-omics and spatial multi-omics data integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct neighborhood graphs based on single-cell expression data and spatial information respectively, and perform self-supervised learning for data integration through the graph attention auto-encoders from two paths. SSGATE is applied to data integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.