GAC: Gene Associations with Clinical, a web based application

• Type of Material: Article
• Authors: Xinyan Zhang, Emory University; Manali Rupji, Emory University; Jeanne Kowalski, Emory University
• Language: English
• Date: 2017-01-01
• Publisher: F1000Research
• Publication Version: Final Published Version
• Copyright Statement: © 2017 Zhang X et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.12688/f1000research.11840.2
• Title of Journal or Parent Work: F1000Research
• ISSN: 2046-1402
• Volume: 6
• Start Page: 1039
• End Page: 1039
• Grant/Funding Information: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.; Research reported in this publication was supported in part by the Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University and NIH/NCI under award number P30CA138292.
• Keywords: continuous; SuperPC; binary outcome; time-to-event; forest plot
• Research Categories: Biology, Genetics; Biology, Bioinformatics

Abstract

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 the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.