RLE plots: Visualizing unwanted variation in high dimensional data.

Luke C Gandolfo,Terence P Speed,Enrique Hernandez-Lemus

doi:10.1371/journal.pone.0191629

Luke C Gandolfo, Terence P Speed + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0191629

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Abstract

Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualizing such variation in high dimensional data. We provide a detailed examination of these plots, with the aid of examples and simulation, explaining what they are and what they can reveal. RLE plots are particularly useful for assessing whether a procedure aimed at removing unwanted variation, i.e. a normalization procedure, has been successful. These plots, while originally devised for gene expression data from microarrays, can also be used to reveal unwanted variation in many other kinds of high dimensional data, where such variation can be problematic.

Highlights

Relative log expression (RLE) plots are a simple, yet powerful, tool for visualizing unwanted variation in high dimensional data
We have seen that RLE plots, i.e. boxplots of deviations from gene medians, provide a simple, yet powerful, tool for detecting and visualizing unwanted variation in high dimensional microarray data, the presence of which is often problematic
The only assumption we need to interpret sample heterogeneity in an RLE plot as a sign of unwanted variation is that expression levels of a majority of genes are unaffected by the biological factors of interest

Summary

Introduction

Relative log expression (RLE) plots are a simple, yet powerful, tool for visualizing unwanted variation in high dimensional data. They were originally devised for analyzing data from gene expression studies involving microarrays, e.g. The physical causes of such variation will typically be unknown This unwanted variation can be so large that comparing gene expression values between samples, often the main objective of such a study, can no longer be sensibly done; doing so can lead to false positives, false negatives, or both. It is crucially important to be able to detect the presence of unwanted variation This is what RLE plots were devised to do

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