.png){kind=icon}
.png){kind=icon}
Return
Spatial transcriptomic alignment, integration, and 3D reconstruction by STAIR
.png){kind=icon}
OA
AI
DOI：10.1186/s13059-025-03895-x.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Merging multiple slices into a unified 3D atlas is a significant challenge in spatial transcriptomics. Here, we introduce STAIR, an end-to-end solution for alignment, integration, and 3D reconstruction. STAIR employs a heterogeneous graph attention network with spot-level and slice-level attention mechanisms to achieve a unified embedding space and guide unsupervised 3D reconstruction. We demonstrate STAIR's marked improvements in feature integration and 2D alignment across samples and platforms compared to previous methods. Furthermore, STAIR shows first-of-its-kind performance in z-axis reconstruction of parallel slices and seamlessly integrates new slices into existing 3D atlases, providing novel biological insights from a 3D perspective.
Keywords:
Spatial transcriptome
3D reconstruction
Alignment
Integration
Heterogeneous graph attention network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.