  In this paper, we introduce QuST-LLM, an innovative extension of QuPath that
utilizes the capabilities of large language models (LLMs) to analyze and
interpret spatial transcriptomics (ST) data. In addition to simplifying the
intricate and high-dimensional nature of ST data by offering a comprehensive
workflow that includes data loading, region selection, gene expression
analysis, and functional annotation, QuST-LLM employs LLMs to transform complex
ST data into understandable and detailed biological narratives based on gene
ontology annotations, thereby significantly improving the interpretability of
ST data. Consequently, users can interact with their own ST data using natural
language. Hence, QuST-LLM provides researchers with a potent functionality to
unravel the spatial and functional complexities of tissues, fostering novel
insights and advancements in biomedical research. QuST-LLM is a part of QuST
project. The source code is hosted on GitHub and documentation is available at
(https://github.com/huangch/qust).
