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. 2011 Aug 16;4(1):24.
doi: 10.1186/1756-0381-4-24.

An R package implementation of multifactor dimensionality reduction

Stacey J Winham  1 , Alison A Motsinger-Reif
Affiliations

An R package implementation of multifactor dimensionality reduction

Stacey J Winham et al. BioData Min. .

Abstract

Background: A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users.

Results: We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package.

Conclusions: MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users.

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Figures

Figure 1
Figure 1
The result of a sample call to 'plot' after an MDR fit with 5-fold cross-validation on a simulated dataset with 250 individuals genotyped at 25 SNPs.
Figure 2
Figure 2
The result of a sample call to 'plot' after an MDR fit with three-way split on a simulated dataset with 250 individuals genotyped at 25 SNPs.
See this image and copyright information in PMC

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