MultiGATE is a two-level graph-attention auto-encoder designed for spatial multi-omics analysis. It extracts the latent embeddings of the pixels/spots in spatial multi-omics data, while simultaneously incorporating the regulatory relationship of the cross-modality features through the cross-modality attention mechanism and the spatial relationship of the pixels/spots through the within-modality attention mechanism. In addition to reconstruction loss, a CLIP contrastive loss aligns embeddings across modalities. MultiGATE yields (i) latent representations of pixels for clustering and visualization and (ii) cross-modality attention scores for cross-modality regulatory inference.
The /Reproduce directory contains scripts and resources to reproduce the main figures and results of MultiGATE, including spatial clustering results, cis-regulation, trans-regulation analysis, long-range interaction detection, and protein-gene interaction findings. Please refer to the instructions and scripts in this directory to replicate the key analyses and visualizations presented in the paper.
Please follow the Tutorials for installation and Usage.
Miao, Jishuai, Jinzhao Li, Jingxue Xin, Jiajuan Tu, Muyang Ge, Ji Qi, Xiaocheng Zhou, Ying Zhu, Can Yang, and Zhixiang Lin. "MultiGATE: integrative analysis and regulatory inference in spatial multi-omics data via graph representation learning." Nature Communications 16, no. 1 (2025): 9403. https://www.nature.com/articles/s41467-025-63418-x