تقرير
CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis
| العنوان: | CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis |
|---|---|
| المؤلفون: | Xiao, Yihang, Liu, Jinyi, Zheng, Yan, Xie, Xiaohan, Hao, Jianye, Li, Mingzhi, Wang, Ruitao, Ni, Fei, Li, Yuxiao, Luo, Jintian, Jiao, Shaoqing, Peng, Jiajie |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Quantitative Biology - Genomics |
| الوصف: | Single-cell RNA sequencing (scRNA-seq) data analysis is crucial for biological research, as it enables the precise characterization of cellular heterogeneity. However, manual manipulation of various tools to achieve desired outcomes can be labor-intensive for researchers. To address this, we introduce CellAgent (http://cell.agent4science.cn/), an LLM-driven multi-agent framework, specifically designed for the automatic processing and execution of scRNA-seq data analysis tasks, providing high-quality results with no human intervention. Firstly, to adapt general LLMs to the biological field, CellAgent constructs LLM-driven biological expert roles - planner, executor, and evaluator - each with specific responsibilities. Then, CellAgent introduces a hierarchical decision-making mechanism to coordinate these biological experts, effectively driving the planning and step-by-step execution of complex data analysis tasks. Furthermore, we propose a self-iterative optimization mechanism, enabling CellAgent to autonomously evaluate and optimize solutions, thereby guaranteeing output quality. We evaluate CellAgent on a comprehensive benchmark dataset encompassing dozens of tissues and hundreds of distinct cell types. Evaluation results consistently show that CellAgent effectively identifies the most suitable tools and hyperparameters for single-cell analysis tasks, achieving optimal performance. This automated framework dramatically reduces the workload for science data analyses, bringing us into the "Agent for Science" era. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.09811 |
| رقم الأكسشن: | edsarx.2407.09811 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |