RT Journal
A1 Shu, Han
A1 Chen, Jing
A1 Xu, Chang
A1 Hu, Jialu
A1 Wang, Yongtian
A1 Peng, Jiajie
A1 Jiang, Qinghua
A1 Shang, Xuequn
A1 Wang, Tao
T1 Efficient integration of spatial omics data for joint domain detection, matching, and alignment with stMSA
JF Genome Research 
JO Genome Research 
YR 2025 
FD October 01 
VO 35 
IS 10 
SP 2285 
OP 2299 
DO 10.1101/gr.280584.125 
UL http://genome.cshlp.org/content/35/10/2285.abstract 
AB Spatial omics (SOs) are powerful methodologies that enable the study of genes, proteins, and other molecular features within the spatial context of tissue architecture. With the growing availability of SO data sets, researchers are eager to extract biological insights from larger data sets for a more comprehensive understanding. However, existing approaches focus on batch effect correction, often neglecting complex biological patterns in tissue slices, complicating feature integration and posing challenges when combining transcriptomics with other omics layers. Here, we introduce spatial multislice/omics analysis (stMSA), a deep graph contrastive learning model that incorporates graph auto-encoder techniques. stMSA is specifically designed to produce batch-corrected representations while retaining the distinct spatial patterns within each slice, considering both intra- and inter-batch relationships during integration. Extensive evaluations show that stMSA outperforms state-of-the-art methods in distinguishing tissue structures across diverse slices, even when faced with varying experimental protocols and sequencing technologies. Furthermore, stMSA effectively deciphers complex developmental trajectories by integrating spatial proteomics and transcriptomics data and excels in cross-slice matching and alignment for 3D tissue reconstruction.