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Related Concept Videos

Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the test...
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

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A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free

Robert J Millikin1, Michael R Shortreed1, Mark Scalf1

  • 1Department of Chemistry, University of Wisconsin, 1101 University Avenue, Madison, Wisconsin 53706, United States.

Journal of Proteome Research
|April 4, 2020
PubMed
Summary

This study introduces a Bayesian hypothesis test for quantitative proteomics, replacing the Student's t-test. This new method improves the detection of significant protein changes by using an interval null hypothesis.

Keywords:
Bayesian hypothesis testBayesian statisticslabel-free quantificationquantitative proteomicssoftware

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Area of Science:

  • Proteomics
  • Statistical analysis
  • Bioinformatics

Background:

  • Student's t-test is common in quantitative proteomics but its null hypothesis can lead to false positives.
  • The t-test's null hypothesis of zero mean difference often marks small, uninteresting fold-changes as significant.
  • Existing compensations for t-test limitations are suboptimal.

Purpose of the Study:

  • To propose and evaluate a Bayesian hypothesis testing approach as an alternative to the Student's t-test in quantitative proteomics.
  • To develop a method that improves sensitivity and accuracy in detecting true protein changes.
  • To implement the method in user-friendly open-source software.

Main Methods:

  • Developed a Bayesian hypothesis test with an interval null hypothesis, estimated from population statistics.
  • Applied the Bayesian method to two benchmark quantitative proteomics datasets (PXD005590, PXD016470).
  • Implemented the method in FlashLFQ, an open-source software for quantifying bottom-up proteomics data.

Main Results:

  • The Bayesian approach demonstrated improved sensitivity in detecting truly changing proteins compared to the t-test.
  • The method successfully identified a substantially larger number of significant protein changes in benchmark datasets.
  • FlashLFQ software provides rapid, sensitive, and accurate quantification.

Conclusions:

  • A Bayesian hypothesis test with an interval null hypothesis is a superior alternative to the Student's t-test for quantitative proteomics.
  • The developed method enhances the detection of biologically relevant protein alterations.
  • FlashLFQ offers a robust and accessible tool for modern proteomics data analysis.