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R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate

Benoît Liquet1, Leonardo Bottolo2, Gianluca Campanella2

  • 1Laboratoire de Mathématiques et de leurs Applications, Université de Pau et des Pays de l'Adour, UMR CNRS 5142, Pau, France; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, Australia.

Journal of Statistical Software
|March 24, 2018
PubMed
Summary
This summary is machine-generated.

R2GUESS is a new R package that provides a user-friendly interface for the GUESS algorithm, enabling efficient analysis of complex genomic data and biological associations.

Keywords:
Bayesian variable selectionC++OMICs dataRgraphics processing unitmultivariate regression

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Computational Biology

Background:

  • High-dimensional genomic data present computational challenges for analyzing complex phenotypes.
  • Efficient statistical models, like Bayesian variable selection, are needed for genome-wide association studies.
  • Few scalable methods exist for handling hundreds of thousands of genetic predictors.

Purpose of the Study:

  • Introduce R2GUESS, an R package for the GUESS algorithm.
  • Provide a user-friendly interface for GUESS, automating parameterization and data handling.
  • Extend GUESS capabilities with features for data exploration, statistical inference, and visualization.

Main Methods:

  • R2GUESS wraps the C++ source code of the GUESS algorithm.
  • The package automates GUESS parameterization and data management.
  • Includes functionalities for effect size estimation, significance assessment, and output visualization.

Main Results:

  • R2GUESS offers a streamlined interface for the GUESS algorithm.
  • The package enhances data exploration and statistical inference capabilities.
  • Demonstrates statistical performance and flexibility through two illustrative examples.

Conclusions:

  • R2GUESS facilitates the analysis of high-dimensional genomic data.
  • The package enhances the utility and accessibility of the GUESS algorithm.
  • Provides a flexible tool for investigating genomic associations with complex phenotypes.