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Related Concept Videos

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The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
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Related Experiment Video

Updated: Jul 13, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Permutation-based adjustments for the significance of partial regression coefficients in microarray data analysis.

Brandie D Wagner1, Gary O Zerbe, Sharon Mexal

  • 1Department of Preventative Medicine and Biometrics, University of Colorado Health Sciences Center, Denver, Colorado, USA.

Genetic Epidemiology
|July 17, 2007
PubMed
Summary

This study generalizes permutation methods for multiple testing in linear regression models with microarray data. Permutation P-value adjustments are better suited for analyzing gene expression data with confounders than normal theory methods.

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Last Updated: Jul 13, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Microarray data analysis often involves complex statistical adjustments for multiple testing.
  • Linear regression models are frequently used, but accounting for covariates and confounders requires robust methods.
  • Existing methods may not be optimal for the unique characteristics of high-dimensional biological data.

Purpose of the Study:

  • To generalize permutation methods for adjusting multiple testing of partial regression coefficients in linear models for microarray data.
  • To determine the significance of disease-related gene expression while accounting for non-categorical covariates.
  • To compare permutation-based adjustments with normal theory adjustments in the presence of confounders.

Main Methods:

  • Generalization of permutation methods for multiple testing adjustment.
  • Application of permutation P-value adjustment (Simon et al., 2004).
  • Use of linear regression models to account for covariates, including non-categorical ones.
  • Comparison with normal theory adjustments on a real microarray dataset.

Main Results:

  • Permutation-based adjustments are demonstrated to be more suitable for microarray data analysis.
  • The methods effectively adjust for confounders in gene expression significance testing.
  • Significant differences were observed when comparing permutation and normal theory adjustments.

Conclusions:

  • Permutation methods offer a more robust approach for multiple testing adjustment in linear regression models applied to microarray data.
  • These methods are particularly valuable when dealing with datasets containing confounders.
  • The study highlights the importance of appropriate statistical techniques for accurate gene expression analysis.