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Related Concept Videos

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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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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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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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Critical Region, Critical Values and Significance Level01:16

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The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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Comprehensive Comparative Analysis of Local False Discovery Rate Control Methods.

Shin June Kim1, Youngjae Oh1, Jaesik Jeong1

  • 1Department of Mathematics and Statistics, Chonnam National University, Gwangju 61186, Korea.

Metabolites
|January 20, 2021
PubMed
Summary
This summary is machine-generated.

This study compares statistical methods for finding biomarkers in complex omics data. It evaluates one-dimensional and two-dimensional false discovery rate (FDR) control procedures on simulated and real datasets.

Keywords:
biomarkerfalse discovery ratefamilywise error ratelarge scale inference

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Technological advancements generate complex, large-scale omics data.
  • Analyzing omics data requires advanced statistical techniques for biomarker discovery.
  • Controlling error rates, like the false discovery rate (FDR), is crucial in group comparisons.

Purpose of the Study:

  • To compare the performance of different statistical methods for biomarker identification in omics data.
  • To evaluate one-dimensional versus multi-dimensional false discovery rate (FDR) control procedures.
  • To assess selected FDR methods on both simulated and real-world biological datasets.

Main Methods:

  • Selection of three distinct FDR control methods: Efron et al. (2001) (1D), Ploner et al. (2006) (2D), and Kim et al. (2018) (2D).
  • Inclusion of two variants of Ploner's approach for a comprehensive comparison.
  • Application and performance evaluation on simulated datasets and real omics data.

Main Results:

  • Performance comparison of one-dimensional and two-dimensional FDR control methods.
  • Evaluation of the effectiveness of Efron, Ploner, and Kim's approaches in biomarker discovery.
  • Assessment of method performance across diverse simulated and real data scenarios.

Conclusions:

  • The study provides insights into the comparative performance of various FDR control methods for omics data analysis.
  • Findings can guide the selection of appropriate statistical techniques for biomarker discovery.
  • Highlights the utility of multi-dimensional FDR procedures for complex biological data.