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Related Concept Videos

Bonferroni Test01:10

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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: Jun 21, 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

Moderated effect size and P-value combinations for microarray meta-analyses.

Guillemette Marot1, Jean-Louis Foulley, Claus-Dieter Mayer

  • 1INRA, UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, F-78350, France. guillemette.marot@jouy.inra.fr

Bioinformatics (Oxford, England)
|July 25, 2009
PubMed
Summary
This summary is machine-generated.

Meta-analysis of microarray data enhances statistical power for limited sample sizes. P-value combination methods offer superior gene ranking and sensitivity compared to effect size approaches in meta-analysis.

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Last Updated: Jun 21, 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
  • Genomics
  • Statistical Genetics

Background:

  • Microarray experiments are increasingly common, with data readily available in public repositories.
  • Meta-analysis is crucial for combining results from multiple microarray studies, especially those with limited sample sizes.
  • Combining studies enhances statistical power and improves the accuracy of results in genomic research.

Purpose of the Study:

  • To evaluate and compare different meta-analysis methods for combining gene expression data from microarrays.
  • To assess the performance of a novel moderated effect size combination method against existing approaches.
  • To determine the most effective meta-analysis strategy for identifying significant genes in cancer research.

Main Methods:

  • A moderated effect size combination method was developed and implemented.
  • Multiple meta-analysis techniques, including P-value combination and effect size methods, were applied.
  • Methods were validated using real-world prostate cancer microarray datasets and an extensive simulation study.

Main Results:

  • The proposed moderated effect size method showed improvement over existing effect size approaches.
  • P-value combination methods demonstrated superior sensitivity and gene ranking compared to effect size methods.
  • Effect size methods were observed to be more conservative in their findings.

Conclusions:

  • P-value combination is a highly effective strategy for meta-analysis of microarray data, offering better sensitivity and gene ranking.
  • While moderated effect size methods offer improvements, P-value combination generally outperforms them for gene discovery.
  • The meta-analysis of gene expression data, particularly using P-value combination, is vital for robust findings in cancer genomics. An R package, metaMA, is available.