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Rotation-based multiple testing in the multivariate linear model.

Aldo Solari1, Livio Finos, Jelle J Goeman

  • 1Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy.

Biometrics
|October 2, 2014
PubMed
Summary
This summary is machine-generated.

Confounding in observational microarray studies is addressed by rotation-based multiple testing. This method extends permutation testing, allowing adjustments for confounders in complex models like those used for breast cancer tumor analysis.

Keywords:
ExchangeabilityFamilywise error rateMicroarrayMultiple testingPermutation testRotation test

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Genomics

Background:

  • Observational microarray studies often face confounding variables that can bias results.
  • Multivariate linear models are used to adjust for measured confounders, but standard permutation testing is problematic due to violated exchangeability assumptions.

Purpose of the Study:

  • To introduce and validate a rotation-based multiple testing procedure for observational microarray studies.
  • To extend permutation-based multiple testing methods to accommodate confounding variables.
  • To provide a robust statistical framework for analyzing complex genomic data.

Main Methods:

  • Developed a rotation-based multiple testing procedure that allows for adjustments for confounding.
  • The method achieves rotatability of transformed responses under a distributional assumption.
  • Applied the methodology to an observational microarray study of breast cancer tumors.

Main Results:

  • Demonstrated that rotation-based multiple testing can effectively adjust for confounding in microarray data.
  • The proposed method offers an important extension to existing permutation-based multiple testing procedures.
  • The approach is illustrated using real-world breast cancer tumor data.

Conclusions:

  • Rotation-based multiple testing provides a valuable tool for analyzing observational microarray data with confounding.
  • This methodology enhances the reliability of statistical inference in genomic studies.
  • The 'flip' R package is available for implementing this advanced statistical procedure.