GAC: Gene Associations with Clinical, a web based application
作者: Xinyan ZhangManali RupjiJeanne Kowalski
作者单位: 1Winship Cancer Institute of Emory University, Atlanta, GA, 30322, USA
2Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA
刊名: F1000Research, 2017, Vol.6
来源数据库: F1000 Research Ltd
DOI: 10.12688/f1000research.11840.1
关键词: SuperPCbinary outcomecontinuoustime-to-eventforest plot
原始语种摘要: We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC. Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data. In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize...
全文获取路径: F1000 Research Ltd 

  • GAC General Access Copy
  • web 梁腹板
  • visualization 目测
  • application 申请
  • visualize 目视
  • package 外壳
  • event 事件
  • interactive 相互作用的
  • repository 储存库
  • summarizing 求和