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Related Experiment Videos

A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability.

Herman M J Sontrop1, Perry D Moerland, René van den Ham

  • 1Molecular Diagnostics Department, Eindhoven, the Netherlands. Herman.Sontrop@philips.com

BMC Bioinformatics
|November 28, 2009
PubMed
Summary
This summary is machine-generated.

Feature variability significantly impacts breast cancer gene signatures and patient classification. Accounting for measurement error and preprocessing choices is crucial for reliable microarray analysis.

Related Experiment Videos

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Microarray breast cancer studies show discrepancies due to platform and biological variability.
  • Measurement error and preprocessing methods are often overlooked contributors to these discrepancies.

Purpose of the Study:

  • To investigate the impact of feature variability on microarray breast cancer classification.
  • To assess the influence of measurement error and preprocessing choices on signature composition and outcome concordance.

Main Methods:

  • Comprehensive sensitivity analysis of breast cancer classification.
  • Utilized data from eight datasets (1131 hybridizations) and six preprocessing methods.
  • Tested classifier stability and noise tolerance using perturbed expression profiles.

Main Results:

  • Feature variability strongly influences signature composition, independent of array platform or patient stratification.
  • High discordance in individual sample classifications arises from different preprocessing schemes, even with identical signatures.
  • Classifier stability is significantly affected by feature variability.

Conclusions:

  • Feature variability critically impacts breast cancer signature composition and individual patient classification.
  • Recommends incorporating feature variability analysis in microarray breast cancer expression profiling.