STAT: A multi-agent framework for integrated and interactive spatial transcriptomics analysis

Spatial transcriptomics analysis often involves a myriad of computational methods across diverse platforms, leading analysts to spend excessive time on data assembly rather than deriving biological insights. Current AI solutions tend to either oversimplify spatial data into generic single-cell tables or operate autonomously without opportunities for intermediate review, thus hindering the visual and iterative analyses essential for spatial biology. In response to these challenges, we introduce STAT, a multi-agent framework designed to make spatial analysis more conversational and user-friendly while maintaining transparency and control. STAT integrates a persistent session, a shared interactive tissue viewer, and a staged skill-aware pipeline, enabling a more intuitive analytical experience. In a comprehensive benchmark evaluation encompassing eleven analytical task categories across three spatial platforms and both cell- and spot-resolution data, STAT demonstrates superior performance compared to a vanilla large language model and existing autonomous spatial analysis agents, excelling in task completion, analytical quality, and token efficiency. Notably, STAT enables multi-task spatial analysis of a mixed-resolution breast cancer cohort and successfully reproduces key findings from a published Visium HD colorectal cancer study using natural language prompts alone. STAT thus facilitates trustworthy and scientifically rigorous spatial transcriptomics analysis, allowing researchers to focus more on biological interpretation.