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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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GAC: Gene Associations with Clinical, a web based application.

Xinyan Zhang1, Manali Rupji1, Jeanne Kowalski1,2

  • 1Winship Cancer Institute of Emory University, Atlanta, GA, 30322, USA.

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|March 7, 2018
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Summary

We developed GAC, an R-based tool for visualizing clinical associations using high-dimensional data. It enhances supervised principal component analysis (SuperPC) for various clinical outcomes, aiding genomic data interpretation.

Keywords:
SuperPCbinary outcomecontinuousforest plottime-to-event

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • High-dimensional data, such as gene expression, is crucial for understanding clinical associations.
  • Existing methods for analyzing high-dimensional data with clinical outcomes can be limited in scope and flexibility.
  • Supervised Principal Component Analysis (SuperPC) offers a powerful approach but requires extensions for broader applicability.

Purpose of the Study:

  • To introduce GAC, an interactive R-based tool for visualizing clinical associations.
  • To extend SuperPC methodology to accommodate binary, continuous, and time-to-event clinical outcomes.
  • To provide a user-friendly platform for integrating high-dimensional data with clinical data analysis.

Main Methods:

  • Development of a shiny R package named GAC.
  • Implementation of an extended SuperPC approach within the GAC tool.
  • Integration of interactive forest plot visualizations for summarizing results.

Main Results:

  • GAC enables interactive visualization of clinical associations using high-dimensional data.
  • The tool supports SuperPC analysis for binary, continuous, and time-to-event outcomes.
  • Interactive forest plots facilitate the interpretation of results for various data types.

Conclusions:

  • GAC offers a comprehensive solution for identifying and visualizing associations between clinical outcomes and high-dimensional data.
  • The enhanced SuperPC approach increases the flexibility and utility of analyzing complex biological datasets.
  • The GAC package is readily available for researchers in R, promoting wider adoption.