Work

Alignment of spatial transcriptomics slices across diseases, platforms and conditions

Year: 2026

Type: article

Abstract: Spatial transcriptomics (ST) facilitates the exploration of biological tissue structures and functions within spatial context. Slice alignment and integration are prevalent for analyzing ST data, and current algorithms either focus on adjacent slices or require prior information to guide alignment, limits their applications for downstream analysis. Here, we present AlignDG, an information theory-based graph model that jointly aligns and integrates ST slices across diverse diseases, platforms and conditions without prior information. Experimental results demonstrate that AlignDG outperforms existing baselines in terms of precision, robustness, and efficiency with approximate 50% of slices, providing an effective alternative for analyzing ST data (code: https://github.com/xkmaxidian/AlignDG ).

Source: Genome Medicine

DOI: 10.1186/s13073-026-01634-w

Authors: Yu Wang, Zaiyi Liu, Qingchen Zang, Xiaoke Ma

Institutions: Xidian University, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Southern Medical University, Hainan University

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,290

Funders: National Natural Science Foundation of China, Xidian University, National Science and Technology Major Project, Fundamental Research Funds for the Central Universities