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Key Takeaways

  • SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis Trajectory inference (TI) is a vital process in computational biology u...
  • Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics.
  • As the fundamental paradigm for reconstructing cell developmental paths, trajectory inference (TI) is critical.
  • However, existing methods require extensive manual intervention and proficiency in heterogeneous tools, posing a significant barrier to efficient TI analysis.
  • To bridge this gap, we propose SpaCellAgent, an autonomous large language model (LLM) multi-agent framework that automates end-to-end spatiotemporal analysis and narrative generation.
Paper AbstractExpand

Spatial and Single-cell transcriptomics are transformative in deciphering cellular dynamics. As the fundamental paradigm for reconstructing cell developmental paths, trajectory inference (TI) is critical. However, existing methods require extensive manual intervention and proficiency in heterogeneous tools, posing a significant barrier to efficient TI analysis. To bridge this gap, we propose SpaCellAgent, an autonomous large language model (LLM) multi-agent framework that automates end-to-end spatiotemporal analysis and narrative generation. SpaCellAgent utilizes a multi-agent architecture for strategic workflow planning, a dynamic tool-orchestration engine for adaptive algorithm selection, and a self-evolution module that iteratively refines performance through feedback. We evaluate SpaCellAgent on six heterogeneous datasets encompassing complex temporal developmental trajectories, diverse sequencing platforms, and spatially-resolved tissue architectures. SpaCellAgent consistently demonstrates over 40\% improvement in analytical efficiency while maintaining expert-aligned performance. By converting natural language specifications into optimized analytical workflows and fully automating the pipeline, SpaCellAgent democratizes advanced spatiotemporal modeling and establishes a scalable, agent-driven paradigm for computational biology. The code and materials are available at this https URL .

SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis
Trajectory inference (TI) is a vital process in computational biology used to reconstruct how cells develop and change over time. Traditionally, this analysis is a complex, manual task that requires experts to navigate a fragmented landscape of heterogeneous tools and algorithms. SpaCellAgent addresses this by introducing an autonomous, multi-agent framework powered by Large Language Models (LLMs). It automates the entire end-to-end pipeline—from processing raw spatial and single-cell transcriptomics data to generating final biological narratives—thereby removing the need for extensive manual intervention.

A Collaborative Multi-Agent Architecture

SpaCellAgent functions through a team of specialized agents that mimic human collaboration to handle complex tasks:

  • Planner: Analyzes the user's goals and the dataset's characteristics to break the project down into a logical sequence of steps.

  • Executor: Uses a dynamic tool-orchestration engine to select the best algorithms for the specific data and writes the necessary code to run the analysis.

  • Evaluator: Performs quality control by checking both the code for errors and the biological results for scientific plausibility, such as ensuring that developmental paths are not logically inverted.

  • Reporter: Synthesizes the findings into clear, interpretable biological insights.

Self-Refinement and Knowledge Growth

A key feature of the framework is its ability to learn and improve over time. It employs a self-refinement mechanism that allows the system to detect errors in its own code or biological logic and automatically attempt corrections. Furthermore, the system uses a dual-layer memory architecture: local memory tracks the current task's progress, while global memory stores successful workflows and fixes. This allows the agent to "evolve" by accumulating knowledge from past analyses, enabling it to handle new, diverse datasets more effectively as it gains experience.

Efficiency and Performance

SpaCellAgent was tested on six diverse datasets, including various sequencing platforms and tissue architectures. The results show that the framework consistently matches the performance of human experts while significantly improving productivity. Specifically, the system demonstrated a 41.2% increase in analytical efficiency, drastically reducing the time required to move from raw data to meaningful scientific discovery.

Bridging the Gap in Bioinformatics

By converting natural language instructions into optimized, automated workflows, SpaCellAgent democratizes advanced spatiotemporal modeling. It addresses the lack of standardization in the field by providing a closed-loop system that can discover and register new tools as needed. This approach not only makes trajectory analysis more accessible to domain scientists but also establishes a scalable, agent-driven paradigm for future computational biology research.

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