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bioRxiv · 10.1101/062158

MINOTAUR: A platform for the analysis and visualization of multivariate results from genome scans with R Shiny

Robert Verity·Caitlin Collins·Daren C. Card·Sara M Schaal·Liuyang Wang·Kathleen Lotterhos
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Abstract

Genome scans are widely used to identify 'outliers' in genomic data: loci with different patterns compared with the rest of the genome due to the action of selection or other non-adaptive forces of evolution. These genomic datasets are often high-dimensional, with complex correlation structures among variables, making it a challenge to identify outliers in a robust way. The Mahalanobis distance has been widely used for this purpose, but has the major limitation of assuming that data follow a simple parametric distribution. Here we develop three new metrics that can be used to identify outliers in multivariate space, while making no strong assumptions about the distribution of the data. These metrics are implemented in the R package MINOTAUR, which also includes an interactive web-based application for visualizing outliers in high-dimensional datasets. We illustrate how these metrics can be used to identify outliers from simulated genetic data, and discuss some of the limitations they may face in application.

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Robert Verity, Caitlin Collins, Daren C. Card, Sara M Schaal, Liuyang Wang, Kathleen Lotterhos. 2016-07-05. MINOTAUR: A platform for the analysis and visualization of multivariate results from genome scans with R Shiny. https://doi.org/10.1101/062158

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