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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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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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P-value is one of the most crucial concepts in statistics.
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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Myriads: P-value-based multiple testing correction.

Antonio Carvajal-Rodríguez1

  • 1Department of Biochemistry, Genetics and Immunology, Address Facultad de Biologia, University of Vigo, Vigo, Spain.

Bioinformatics (Oxford, England)
|November 30, 2017
PubMed
Summary

The Myriads program offers robust multiple testing correction methods for high-dimensional omic data. It efficiently handles large P-value lists and includes dependency testing, aiding complex biological research.

Area of Science:

  • Bioinformatics
  • Statistical genetics
  • Computational biology

Background:

  • Multiple testing correction is crucial for omics data analysis.
  • Existing methods struggle with high-dimensional P-value lists and data dependencies.
  • Microarray and other omics technologies generate vast amounts of P-values requiring efficient management.

Purpose of the Study:

  • To introduce Myriads, a software tool for P-value-based multiple testing correction.
  • To provide robust methods capable of managing large P-value datasets.
  • To incorporate a dependency test and P-value simulator for comprehensive analysis.

Main Methods:

  • Myriads implements key P-value-based correction methods.
  • The software is designed to manage hundreds of thousands of P-values.

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  • Includes a statistical test for data dependency and a P-value simulator.

    • Main Results:

    • Myriads effectively handles large-scale P-value datasets.
    • Offers robust multiple testing correction methods suitable for omics data.
    • Provides integrated tools for dependency assessment and simulation.

    Conclusions:

    • Myriads is a valuable tool for researchers working with high-dimensional omics data.
    • It addresses limitations of existing methods in managing large P-value lists and dependencies.
    • Facilitates more reliable statistical inference in complex biological studies.