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ggplotAgent: a self-debugging multi-modal agent for robust and reproducible scientific visualization.

Zelin Wang1, Yuanyuan Yin1, Jien Wang2

  • 1Guangdong Provincial Key Laboratory of Cancer Pathogenesis and Precision Diagnosis and Treatment, Joint Big Data Laboratory, Department of Medical Oncology, Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University, Shanwei, 516600, China.

Bioinformatics Advances
|January 16, 2026
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Summary
This summary is machine-generated.

ggplotAgent automates the creation of publication-quality bioinformatics visualizations using artificial intelligence. This self-debugging tool ensures accurate, high-quality plots from natural language, overcoming common coding challenges for researchers.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Visualization

Background:

  • Publication-quality visualizations are crucial in bioinformatics.
  • Limited coding expertise presents a bottleneck for researchers.
  • Existing Large Language Models (LLMs) struggle with code execution errors and dataset mismatches for visualization tasks.

Purpose of the Study:

  • To develop an automated solution for generating publication-ready ggplot2 visualizations.
  • To address the limitations of current LLMs in creating accurate bioinformatics plots.
  • To enable researchers with limited coding skills to produce high-quality data visualizations.

Main Methods:

  • Introduction of ggplotAgent, a multi-modal, self-debugging AI agent.
  • Implementation of a dual-layered framework for resolving code execution errors.
  • Integration of a vision-enabled agent to verify and ensure aesthetic correctness of visualizations.

Main Results:

  • ggplotAgent achieved 100% code executability, surpassing DeepSeek-V3's 85%.
  • A "Publication-Ready" score of 1.9 was obtained, compared to 0.7 for the baseline.
  • The agent demonstrated collaborative capabilities, enhancing plots beyond prompts with a positive Insight Score (+0.3).

Conclusions:

  • ggplotAgent reliably automates the production of accurate, high-quality bioinformatics visualizations.
  • The tool overcomes common LLM limitations, improving efficiency for researchers.
  • Freely accessible web and offline applications facilitate widespread adoption.