PT  - JOURNAL ARTICLE
AU  - Yang, Yitao
AU  - Cui, Yang
AU  - Zeng, Xin
AU  - Zhang, Yubo
AU  - Loza, Martin
AU  - Park, Sung-Joon
AU  - Nakai, Kenta
TI  - STAIG: Spatial Transcriptomics Analysis via Image-Aided Graph Contrastive Learning for Domain Exploration and Alignment-Free Integration
AID  - 10.1101/2023.12.18.572279
DP  - 2024 Jan 01
TA  - bioRxiv
PG  - 2023.12.18.572279
4099  - http://biorxiv.org/content/early/2024/01/02/2023.12.18.572279.short
4100  - http://biorxiv.org/content/early/2024/01/02/2023.12.18.572279.full
AB  - Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a novel deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects. Moreover, it was designed to accept data acquired from various platforms, with or without histological images. By performing extensive benchmarks, we demonstrated the capability of STAIG to recognize spatial regions with high precision and uncover new insights into tumor microenvironments, highlighting its promising potential in deciphering spatial biological intricates.Competing Interest StatementThe authors have declared no competing interest.