RLE plots: Visualizing unwanted variation in high dimensional data

Luke C Gandolfo; Terence P Speed

doi:10.1371/journal.pone.0191629

RLE plots: Visualizing unwanted variation in high dimensional data

PLoS One. 2018 Feb 5;13(2):e0191629. doi: 10.1371/journal.pone.0191629. eCollection 2018.

Authors

Luke C Gandolfo^{1

2}, Terence P Speed^{1

2}

Affiliations

¹ Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.
² School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria, Australia.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computer Simulation*
Gene Expression

Grants and funding

The authors acknowledge support from the National Health and Medical Research Council of Australia (Program Grant 1054618). See https://www.nhmrc.gov.au/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.