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bioRxiv · 10.1101/2024.06.03.597266

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

Lv, T.·Zhang, Y.·Liu, J.·Kang, Q.·Liu, L.
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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.

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Lv, T., Zhang, Y., Liu, J., Kang, Q., Liu, L.. 2024-06-04. Single-cell multi-omics and spatial multi-omics data integration via dual-path graph attention auto-encoder. https://doi.org/10.1101/2024.06.03.597266

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