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

作者：Xiaofan Lu, Kailai Li, Zongcheng Li, Anqi Lin, Long Zhao, Rongfang Shen, Zhou-Geng Xu, Jianing Gao, Dekang Lv, Yasi Zhang, Taojun Ye, Junyi Shen, Yijing Chen, Hui Huang, Zhaodong Hao, Dongqiang Zeng, Haitao Wang, Shipeng Guo, Wen Wang, Yi Xiong, Yin Li, Hao Li, Jingze Gao, Qian Liu, Bin Wei, Jiawei Shi, Shuwen Cheng, Guoqi Li, Yuan Tang, Enyu Lin, Houshi Xu, Guoshuang Feng, Yangzhige He, Yu Sun, Xiaojian Liu, Yufang Wang, Wenxin Song, Jinen Song, Sai Tian, Ya Zhang, Jie Zhang, Zhongtian Xu, Chengli Song, Yingying Zhang, Hao Wu, Chunhui Gao, Erqiang Hu, Chen Yang, Jiacheng Lou, Di Wang, Xuan-Yu Wang, Peng Luo, Guangchuang Yu, Ying Ge · 发表于：iMetaMed · 年份：2025 · DOI：10.1002/imm3.70005 · 被引用次数：8 · 研究领域：Cell Image Analysis Techniques、Biomedical Text Mining and Ontologies、Bioinformatics and Genomic Networks

ABSTRACT Biomedical research data visualization faces several challenges, including insufficient expertise and fragmented methodologies, which severely limit research efficiency and result quality. FigureYa is a standardized visualization framework composed of 317 modular R/python scripts, rather than a standalone software or desktop application. It covers key domains such as expression profiling, immune analysis, survival analysis, and single‐cell data visualization. Based on the concept of “replace data and use,” FigureYa significantly lowers the technical threshold, allowing researchers to generate high‐quality charts without requiring an extensive programming background. Compared to generic online R code snippets, FigureYa offers rigorously developed, thoroughly validated, and biologically contextualized visualization modules originally written by the author team. Each script includes version‐matched environments, example datasets, and detailed annotations, providing clear advantages in automation, reproducibility, and scientific professionalism, thereby providing a standardized visualization solution for complex biomedical data. This innovative tool optimizes research time allocation, promotes interdisciplinary collaboration, accelerates scientific discovery and clinical translation, and provides robust data visualization support for biomedical research.