QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis

TL;DR

QuST-LLM enhances spatial transcriptomics analysis by integrating large language models into a user-friendly platform, enabling detailed biological interpretation and natural language interaction with complex tissue data.

Contribution

It introduces QuST-LLM, a novel extension of QuPath that leverages LLMs for comprehensive, interpretable spatial transcriptomics analysis and natural language data interaction.

Findings

Improves interpretability of spatial transcriptomics data.

Enables natural language interaction with complex biological data.

Provides a comprehensive workflow for tissue analysis.

Abstract

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).