CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis

TL;DR

CellAgent is an LLM-driven multi-agent framework that automates single-cell RNA sequencing data analysis, reducing manual effort and improving result quality through hierarchical decision-making and autonomous optimization.

Contribution

The paper introduces CellAgent, a novel multi-agent system leveraging LLMs for fully automated, high-quality single-cell data analysis with hierarchical coordination and self-optimization.

Findings

Effectively identifies suitable tools and hyperparameters.

Achieves optimal performance on diverse tissue datasets.

Reduces manual workload significantly.

Abstract

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.