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Related Concept Videos

Bonferroni Test01:10

Bonferroni Test

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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.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
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Multiple Comparison Tests01:13

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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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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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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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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
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Testing a Claim about Population Proportion01:24

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Identifying Statistically Significant Differences: The F-Test01:14

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The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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Related Experiment Video

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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Bon-EV: an improved multiple testing procedure for controlling false discovery rates.

Dongmei Li1, Zidian Xie2, Martin Zand3

  • 1Clinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, 14642, NY, USA. dongmei_li@urmc.rochester.edu.

BMC Bioinformatics
|January 5, 2017
PubMed
Summary
This summary is machine-generated.

We introduce Bon-EV, a new multiple testing procedure for genomic studies. Bon-EV enhances stability and false discovery rate control while maintaining high power, outperforming existing methods in larger sample sizes.

Keywords:
FDRMultiple testing procedurePowerRNA-SeqStability

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Area of Science:

  • Genomic data analysis
  • Statistical genetics
  • Bioinformatics

Background:

  • Multiple testing procedures are crucial for controlling false discoveries in genomic studies.
  • Existing methods like Benjamini-Hochberg and Storey's q-value have trade-offs in power and stability.
  • Storey's q-value offers higher power but lower stability compared to Benjamini-Hochberg.

Purpose of the Study:

  • To propose a novel multiple testing procedure, Bon-EV, to enhance stability while maintaining high power.
  • To improve false discovery rate (FDR) control in genomic data analysis.
  • To offer a more consistent alternative to existing procedures, especially for replicated experiments.

Main Methods:

  • Development of the Bon-EV procedure based on Bonferroni's approach.
  • Simulation studies with varying sample sizes (small, medium, large) and correlation levels of test statistics.
  • Application of the Bon-EV procedure to RNA-Seq data.

Main Results:

  • Bon-EV demonstrates high power comparable to Storey's q-value procedure.
  • For medium and large sample sizes, Bon-EV shows improved FDR control and stability over Storey's q-value.
  • Bon-EV exhibits higher power than Benjamini-Hochberg in RNA-Seq data analysis, with enhanced stability.

Conclusions:

  • The Bon-EV procedure offers superior FDR control and stability for medium to large sample sizes.
  • Bon-EV provides increased power compared to Benjamini-Hochberg, maintaining high power similar to Storey's q-value.
  • The Bon-EV procedure is available as an R package (BonEV) for broader application in genomic research.