Shuangbin Xu, Qianwen Wang, Shaodi Wen, Junrui Li, Nan He, Ming Li, Thomas Hackl, Rui Wang, Dongqiang Zeng, Shixiang Wang, Shensuo Li, Chun-Hui Gao, Lang Zhou, Shaoguo Tao, Zijing Xie, Lin Deng, Guangchuang Yu
{"title":"aplot:简化复杂图形的创建,以可视化跨不同数据类型的关联。","authors":"Shuangbin Xu, Qianwen Wang, Shaodi Wen, Junrui Li, Nan He, Ming Li, Thomas Hackl, Rui Wang, Dongqiang Zeng, Shixiang Wang, Shensuo Li, Chun-Hui Gao, Lang Zhou, Shaoguo Tao, Zijing Xie, Lin Deng, Guangchuang Yu","doi":"10.1016/j.xinn.2025.100958","DOIUrl":null,"url":null,"abstract":"<p><p>Effective data visualization is crucial for researchers, revealing patterns, trends, and insights that might otherwise remain hidden. Integrating related visualizations can reveal correlations and relationships that are not evident when analyzing datasets separately. Despite increasing demand, there is a shortage of general tools to seamlessly combine diverse datasets to create complex visual representations. The <i>aplot</i> package addresses this by allowing users to independently create subplots and assemble them into a cohesive composite figure. It automatically reorders datasets for coordinate consistency, removing the need for manual adjustment. This modular approach simplifies the creation of complex visualizations, allowing customization to meet specific needs. <i>Aplot</i>'s versatility is ideal for integrating multi-omics datasets and analytical results for biological insights. The package is freely available on CRAN at https://cran.r-project.org/package=aplot, offering researchers a powerful tool for enhanced data exploration and visualizing workflows.</p>","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":"6 9","pages":"100958"},"PeriodicalIF":25.7000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447650/pdf/","citationCount":"0","resultStr":"{\"title\":\"aplot: Simplifying the creation of complex graphs to visualize associations across diverse data types.\",\"authors\":\"Shuangbin Xu, Qianwen Wang, Shaodi Wen, Junrui Li, Nan He, Ming Li, Thomas Hackl, Rui Wang, Dongqiang Zeng, Shixiang Wang, Shensuo Li, Chun-Hui Gao, Lang Zhou, Shaoguo Tao, Zijing Xie, Lin Deng, Guangchuang Yu\",\"doi\":\"10.1016/j.xinn.2025.100958\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Effective data visualization is crucial for researchers, revealing patterns, trends, and insights that might otherwise remain hidden. Integrating related visualizations can reveal correlations and relationships that are not evident when analyzing datasets separately. Despite increasing demand, there is a shortage of general tools to seamlessly combine diverse datasets to create complex visual representations. The <i>aplot</i> package addresses this by allowing users to independently create subplots and assemble them into a cohesive composite figure. It automatically reorders datasets for coordinate consistency, removing the need for manual adjustment. This modular approach simplifies the creation of complex visualizations, allowing customization to meet specific needs. <i>Aplot</i>'s versatility is ideal for integrating multi-omics datasets and analytical results for biological insights. The package is freely available on CRAN at https://cran.r-project.org/package=aplot, offering researchers a powerful tool for enhanced data exploration and visualizing workflows.</p>\",\"PeriodicalId\":36121,\"journal\":{\"name\":\"The Innovation\",\"volume\":\"6 9\",\"pages\":\"100958\"},\"PeriodicalIF\":25.7000,\"publicationDate\":\"2025-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447650/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Innovation\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xinn.2025.100958\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/9/8 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Innovation","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1016/j.xinn.2025.100958","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/9/8 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
aplot: Simplifying the creation of complex graphs to visualize associations across diverse data types.
Effective data visualization is crucial for researchers, revealing patterns, trends, and insights that might otherwise remain hidden. Integrating related visualizations can reveal correlations and relationships that are not evident when analyzing datasets separately. Despite increasing demand, there is a shortage of general tools to seamlessly combine diverse datasets to create complex visual representations. The aplot package addresses this by allowing users to independently create subplots and assemble them into a cohesive composite figure. It automatically reorders datasets for coordinate consistency, removing the need for manual adjustment. This modular approach simplifies the creation of complex visualizations, allowing customization to meet specific needs. Aplot's versatility is ideal for integrating multi-omics datasets and analytical results for biological insights. The package is freely available on CRAN at https://cran.r-project.org/package=aplot, offering researchers a powerful tool for enhanced data exploration and visualizing workflows.
期刊介绍:
The Innovation is an interdisciplinary journal that aims to promote scientific application. It publishes cutting-edge research and high-quality reviews in various scientific disciplines, including physics, chemistry, materials, nanotechnology, biology, translational medicine, geoscience, and engineering. The journal adheres to the peer review and publishing standards of Cell Press journals.
The Innovation is committed to serving scientists and the public. It aims to publish significant advances promptly and provides a transparent exchange platform. The journal also strives to efficiently promote the translation from scientific discovery to technological achievements and rapidly disseminate scientific findings worldwide.
Indexed in the following databases, The Innovation has visibility in Scopus, Directory of Open Access Journals (DOAJ), Web of Science, Emerging Sources Citation Index (ESCI), PubMed Central, Compendex (previously Ei index), INSPEC, and CABI A&I.