QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis
This provides biomedical researchers with a tool to better understand tissue complexities, though it is incremental as it builds on existing QuPath and LLM methods.
The paper tackles the complexity of spatial transcriptomics data by introducing QuST-LLM, an extension of QuPath that uses large language models to analyze and interpret the data, improving interpretability through natural language interactions.
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).