{"title":"QuST-LLM:整合大型语言模型进行综合空间转录组学分析","authors":"Chao Hui Huang","doi":"arxiv-2406.14307","DOIUrl":null,"url":null,"abstract":"In this paper, we introduce QuST-LLM, an innovative extension of QuPath that\nutilizes the capabilities of large language models (LLMs) to analyze and\ninterpret spatial transcriptomics (ST) data. This tool effectively simplifies\nthe intricate and high-dimensional nature of ST data by offering a\ncomprehensive workflow that includes data loading, region selection, gene\nexpression analysis, and functional annotation. QuST-LLM employs LLMs to\ntransform complex ST data into understandable and detailed biological\nnarratives based on gene ontology annotations, thereby significantly improving\nthe interpretability of ST data. Consequently, users can interact with their\nown ST data using natural language. Hence, QuST-LLM provides researchers with a\npotent functionality to unravel the spatial and functional complexities of\ntissues, fostering novel insights and advancements in biomedical research.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":"15 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis\",\"authors\":\"Chao Hui Huang\",\"doi\":\"arxiv-2406.14307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we introduce QuST-LLM, an innovative extension of QuPath that\\nutilizes the capabilities of large language models (LLMs) to analyze and\\ninterpret spatial transcriptomics (ST) data. This tool effectively simplifies\\nthe intricate and high-dimensional nature of ST data by offering a\\ncomprehensive workflow that includes data loading, region selection, gene\\nexpression analysis, and functional annotation. QuST-LLM employs LLMs to\\ntransform complex ST data into understandable and detailed biological\\nnarratives based on gene ontology annotations, thereby significantly improving\\nthe interpretability of ST data. Consequently, users can interact with their\\nown ST data using natural language. Hence, QuST-LLM provides researchers with a\\npotent functionality to unravel the spatial and functional complexities of\\ntissues, fostering novel insights and advancements in biomedical research.\",\"PeriodicalId\":501070,\"journal\":{\"name\":\"arXiv - QuanBio - Genomics\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2406.14307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.14307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在本文中,我们介绍了 QuST-LLM,它是 QuPath 的创新扩展,利用大型语言模型(LLM)的功能来分析和解释空间转录组学(ST)数据。该工具提供了一个全面的工作流程,包括数据加载、区域选择、基因表达分析和功能注释,从而有效简化了空间转录组学数据的复杂性和高维性。QuST-LLM 利用 LLM 将复杂的 ST 数据转化为基于基因图谱注释的可理解的详细生物学叙述,从而大大提高了 ST 数据的可解释性。因此,用户可以使用自然语言与自己的 ST 数据进行交互。因此,QuST-LLM 为研究人员提供了揭示问题的空间和功能复杂性的强大功能,促进了生物医学研究的新见解和新进展。
QuST-LLM: Integrating Large Language Models for Comprehensive Spatial Transcriptomics Analysis
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. This tool effectively simplifies
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.