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Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust

Daniel Skubleny1, Sunita Ghosh2,3, Jennifer Spratlin2

  • 1Department of Surgery, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, T6G 2R3, Canada. skubleny@ualberta.ca.

BMC Bioinformatics
|March 29, 2024
PubMed
Summary

Feature Specific Quantile Normalization (FSQN) and Feature Specific Mean Variance Normalization (FSMVN) effectively normalize cross-platform gene expression data. Both methods demonstrate equivalent performance for machine learning classification, minimizing technological bias.

Keywords:
Cross-platform normalizationFSMVNFSQNFeature selectionMeanMicroarrayMolecular classificationQuantile normalizationRNAseqVariance

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cross-platform normalization is crucial for integrating microarray and RNA-Seq whole-transcriptome data.
  • Minimizing technological bias enhances the reliability of external validation and machine learning model training sets.
  • This study compares Feature Specific Quantile Normalization (FSQN) against an uncharacterized Feature Specific Mean Variance Normalization (FSMVN) method.

Purpose of the Study:

  • To evaluate and compare the performance of FSQN and FSMVN for bidirectional normalization of cross-platform gene expression data.
  • To assess the impact of nested feature selection on the normalization methods.
  • To determine if these normalization techniques can eliminate batch effects between different technological platforms.

Main Methods:

  • Feature Specific Quantile Normalization (FSQN) and Feature Specific Mean Variance Normalization (FSMVN) were applied to whole-transcriptome data.
  • Bidirectional normalization was performed in the context of nested feature selection.
  • Principal Component Analysis (PCA) was used to assess batch effect removal.
  • Multivariable linear regression analysis was employed to compare model performance.

Main Results:

  • FSQN and FSMVN achieved clinically equivalent bidirectional model performance for colon CMS and breast PAM50 classification, with or without feature selection.
  • PCA confirmed that both methods effectively eliminate batch effects related to technological platforms.
  • Without feature selection, FSQN and FSMVN showed no statistical difference compared to within-platform data distributions.
  • Under optimal feature selection, balanced accuracy for FSQN and FSMVN was statistically equivalent to within-platform performance.

Conclusions:

  • FSQN and FSMVN are equally effective for generating supervised machine learning classifiers for molecular subtypes.
  • When applied under optimal modeling conditions, these methods provide equivalent cross-platform normalization accuracy compared to within-platform data.
  • Caution is advised when using cross-platform data due to potential subtle performance differences based on the specific classification problem and data distributions.