ArticlebioRxiv : the preprint server for biology2026
SPEAK: Spatial Prompting with Expert Aligned Knowledge for Tissue Domain Identification in Spatial Transcriptomics.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Spatially resolved transcriptomic (SRT) data requires spatial domain identification to enable tissue microenvironment-specific downstream analyses. Here we present SPEAK (Spatial Prompting with Expert-Aligned Knowledge), a large language model (LLM) -based method to identify spatial domains from SRT data by taking advantage of the prior knowledge from both LLM and human experts. SPEAK constructs a spatial context prompt for each cell/spot based on cell types and marker genes of its neighboring cells, enabling zero-shot inference, expert-guided fine-tuning, and prototype updating through two-stage prompting. Applications to STARmap, Visium, MERFISH and Xenium datasets showed advantages of SPEAK over existing spatial domain identification methods in domain prediction accuracy, robustness to limited prior knowledge, biological interpretability, and capacity for efficient expert-guided fine-tuning with generalizability to other tissue sections.
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