# RLE Plots: Visualising Unwanted Variation in High Dimensional Data

## TL;DR

RLE plots are a valuable visualization tool for detecting and assessing unwanted variation in high-dimensional data, especially in gene expression studies, and can evaluate the effectiveness of normalization procedures.

## Contribution

This paper provides a detailed analysis and explanation of RLE plots, demonstrating their utility across various high-dimensional data types beyond microarrays.

## Key findings

- RLE plots effectively reveal unwanted variation.
- They help assess normalization success.
- Applicable to diverse high-dimensional datasets.

## Abstract

Unwanted variation can be highly problematic and so its detection is often crucial. Relative log expression (RLE) plots are a powerful tool for visualising 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 normalisation 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.