Detecting anomalous anatomic regions in spatial transcriptomics with STANDS
Kaichen Xu, Yan Lu, Suyang Hou, Kainan Liu, Yihang Du, Mengqian Huang, Hao Feng, Hao Wu, Xiaobo Sun
Abstract
Detection and Dissection of Anomalous Tissue Domains (DDATD) from multi-sample spatial transcriptomics (ST) data provides unprecedented opportunities to characterize anomalous tissue domains (ATDs), revealing both population-level and individual-specific pathogenic factors for understanding pathogenic heterogeneities behind diseases. However, no current methods can perform de novo DDATD from ST data, especially in the multi-sample context. Here, we introduce STANDS, an innovative framework based on Generative Adversarial Networks which integrates three core tasks in multi-sample DDATD: detecting, aligning, and subtyping ATDs. STANDS incorporates multimodal-learning, transfer-learning, and style-transfer techniques to effectively address major challenges in multi-sample DDATD, including complications caused by unalignable ATDs, under-utilization of multimodal information, and scarcity of normal ST datasets necessary for comparative analysis. Extensive benchmarks from diverse datasets demonstrate STAND's superiority in identifying both common and individual-specific ATDs and further dissecting them into biologically distinct subdomains. STANDS also provides clues to developing ATDs visually indistinguishable from surrounding normal tissues.