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RLE plots: Visualizing unwanted variation in high dimensional data.

Luke C Gandolfo1,2, Terence P Speed1,2

  • 1Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia.

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|February 6, 2018
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Summary
This summary is machine-generated.

Relative log expression (RLE) plots are essential for detecting unwanted variation in high-dimensional data. These plots effectively assess the success of normalization procedures across various scientific fields.

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Area of Science:

  • Data analysis
  • Bioinformatics
  • Statistical visualization

Background:

  • Unwanted variation presents significant challenges in high-dimensional data analysis.
  • Detecting and quantifying such variation is critical for reliable scientific conclusions.
  • Relative log expression (RLE) plots offer a method for visualizing data variation.

Purpose of the Study:

  • To provide a comprehensive examination of Relative Log Expression (RLE) plots.
  • To explain the utility of RLE plots in identifying unwanted variation.
  • To demonstrate the effectiveness of RLE plots in evaluating normalization procedures.

Main Methods:

  • Detailed examination of RLE plot principles.
  • Illustrative examples and simulation studies.
  • Application to high-dimensional datasets, including gene expression data.

Main Results:

  • RLE plots are effective tools for visualizing systematic variation within datasets.
  • These plots clearly indicate the success or failure of data normalization techniques.
  • The utility of RLE plots extends beyond microarray data to other high-dimensional data types.

Conclusions:

  • RLE plots are a valuable diagnostic tool for assessing data quality and normalization efficacy.
  • Their application can improve the reliability of analyses involving high-dimensional data.
  • Understanding and utilizing RLE plots is crucial for researchers working with complex datasets.