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quantro: a data-driven approach to guide the choice of an appropriate normalization method.

Stephanie C Hicks1,2, Rafael A Irizarry3,4

  • 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115-5450, USA. shicks@jimmy.harvard.edu.

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Summary
This summary is machine-generated.

Quantile normalization is a common method for high-throughput data analysis, but it may remove biological variation. We propose a data-driven alternative, quantro, to address this limitation in multi-sample analyses.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Data Analysis

Background:

  • Normalization is crucial for high-throughput data analysis, aiming to remove technical variation.
  • Current multi-sample global normalization methods, like quantile normalization, assume all global changes are technical, risking removal of biological signals.
  • Subject matter experts must currently validate the appropriateness of these assumptions.

Purpose of the Study:

  • To propose a data-driven alternative to traditional global normalization methods for high-throughput data.
  • To introduce a novel method that mitigates the risk of removing biologically relevant variation.
  • To provide a software implementation for broader accessibility and application.

Main Methods:

  • Development of a data-driven normalization approach, termed 'quantro'.
  • Utilizing examples and simulations to demonstrate the method's performance and utility.
  • Comparison with existing global normalization techniques to highlight advantages.

Main Results:

  • The proposed quantro method offers a data-driven approach to normalization.
  • Demonstrated utility through various examples and simulation studies.
  • Quantro effectively distinguishes between technical and biological variation, preserving biological signals.

Conclusions:

  • Quantro provides a more robust and biologically informed normalization strategy for high-throughput data.
  • The method reduces the risk of erroneously removing biologically driven variation inherent in global normalization.
  • A software implementation is available, facilitating the adoption of this improved normalization technique.