FigureYa: A Standardized Visualization Framework for Enhancing Biomedical Data Interpretation and Research Efficiency

FigureYa is a standardized visualization framework composed of 317 modular R/Python scripts, covering expression profiling, immune analysis, survival analysis, and single-cell data visualization. The core idea is "replace data and use" — generate high-quality, publication-ready figures without extensive programming background.

This repository provides the complete set of input files, analysis code, and results from the FigureYa manuscript.

🚀 Quick Start

I want to…	See here
Understand why use FigureYa (instead of letting AI write code from scratch)	Why Use FigureYa When AI Can Write Code
Pick the right module out of 317	How to Choose a FigureYa Module
Run a module and use my own data	How to Run FigureYa in Wisp Science
Learn about the recommended environment	Why We Now Recommend Wisp Science

🔎 Interactive Results Browser

All generated reports are hosted on a dedicated webpage with full-text search — also the easiest way to pick a module:

Main site: >https://ying-ge.github.io/FigureYa/
China mirror (faster): >https://figureya.online/

Using the Search

The search box at the top of the page supports full-text search across all HTML reports:
- Enter a keyword (function name, R package, analysis title, or figure type)
- See a snippet of context where the keyword appears
- Get a direct link to the specific FigureYa report

Manual Browsing

Click the thumbnails and HTML links to browse each report. Hover over a thumbnail to reveal the download option for that individual module.

📦 Getting the Code and Data

See How to Run FigureYa in Wisp Science for full instructions. Key options:

Wisp Science chat window (easiest): ask in natural language — e.g. "download FigureYa123mutVSexpr" — or describe your data and let it find the right module.
Git clone (recommended): git clone https://github.com/ying-ge/FigureYa.git
Download ZIP: repo home → Code → Download ZIP
Fork then clone: fork to your account first, then clone, for secondary development
Download a single module from the gallery: hover a thumbnail → download; China users can use >figureya.online for faster access

Some modules' input files exceed GitHub's file size limit. For those larger files, join the Baidu Cloud group 979397987 to access the download links.

📁 Structure of a FigureYa Directory

Each FigureYa directory follows a consistent structure:

Core Files

*.Rmd: The R Markdown script, containing the main analysis and plotting code.
*.html: The knitted report generated from the R Markdown file.
install_dependencies.R: A script automatically run by the .Rmd file to set up the required environment.

Input Files

easy_input_*: Primary data/parameters (e.g., easy_input_data.csv)
example.png: Reference image specifying plot requirements (style/layout)

Output Files

*.pdf: Vector graphic results (editable, publication-ready)
output_*: Text/tables (e.g., output_results.txt)

Example (`FigureYa59volcanoV2`)

FigureYa59volcanoV2/
├── FigureYa59volcanoV2.Rmd          # Main analysis script
├── install_dependencies.R           # Automatic dependency installer
├── FigureYa59volcanoV2.html         # HTML report
├── easy_input_limma.csv             # Input data
├── easy_input_selected.csv          # Input data
├── Volcano_classic.pdf              # Vector graphic (PDF)
├── Volcano_advanced.pdf             # Vector graphic (PDF)
└── example.png                      # Style reference for plots

✍️ Usage and Citation

This project is licensed under the >Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

If you use this project in your research, please cite:

Xiaofan Lu, et al. (2025). FigureYa: A Standardized Visualization Framework for Enhancing Biomedical Data Interpretation and Research Efficiency. iMetaMed, 1(1), e70005. https://doi.org/10.1002/imm3.70005

BibTeX entry:

@article{https://doi.org/10.1002/imm3.70005,
author = {Lu, Xiaofan and Li, Kailai and Li, Zongcheng and Lin, Anqi and Zhao, Long and Shen, Rongfang and Xu, Zhougeng and Gao, Jianing and Lv, Dekang and Zhang, Yasi and Ye, Taojun and Shen, Junyi and Chen, Yijing and Huang, Hui and Hao, Zhaodong and Zeng, Dongqiang and Wang, Haitao and Guo, Shipeng and Wang, Wen and Xiong, Yi and Li, Yin and Li, Hao and Gao, Jingze and Liu, Qian and Wei, Bin and Shi, Jiawei and Cheng, Shuwen and Li, Guoqi and Tang, Yuan and Lin, Enyu and Xu, Houshi and Feng, Guoshuang and He, Yangzhige and Sun, Yu and Liu, Xiaojian and Wang, Yufang and Song, Wenxin and Song, Jinen and Tian, Saisai and Zhang, Ya and Zhang, Jie and Xu, Zhongtian and Song, Chengli and Zhang, Yingying and Wu, Hao and Gao, Chunhui and Hu, Erqiang and Yang, Chen and Lou, Jiacheng and Wang, Di and Wang, Xuanyu and Luo, Peng and Yu, Guangchuang and Ge, Ying},
title = {FigureYa: A Standardized Visualization Framework for Enhancing Biomedical Data Interpretation and Research Efficiency},
journal = {iMetaMed},
volume = {1},
number = {1},
pages = {e70005},
doi = {https://doi.org/10.1002/imm3.70005},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/imm3.70005},
year = {2025}
}

🌱 FigureYa Ecosystem

The FigureYa community has developed many extension tools — see FigureYa Ecosystem:

>Scientific Figure Library (SFL): a local-first MCP service that helps you search, preview, and pick the right FigureYa module
>FigureYa Agent: a Codex-based plotting agent (data profiling + template comparison + script adaptation)
>FigureYaLLM: the first LLM extension, now evolved into SFL

Discover more tools or contribute your own!

🤝 To Be Continued

FigureYa is continuously being updated with improvements to existing modules and the addition of new ones. We welcome interested contributors to join the development of FigureYa!