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Related Concept Videos

Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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2D NMR: Overview of Homonuclear Correlation Techniques01:16

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Homonuclear correlation spectroscopy (COSY) is a powerful technique used in Nuclear Magnetic Resonance (NMR) spectroscopy to study the correlations between nuclei of the same type within a molecule. It provides information about scalar couplings between adjacent nuclei, which helps determine connectivity and structural information. There are several COSY variants, each with its unique strengths and experimental parameters.
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2D NMR: Overview of Heteronuclear Correlation Techniques01:18

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Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other...
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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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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
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Updated: Jul 11, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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Limitation of permutation-based differential correlation analysis.

Hoseung Song1, Michael C Wu1

  • 1Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

Genetic Epidemiology
|November 10, 2023
PubMed
Summary
This summary is machine-generated.

Differential correlation analysis (DCA) using permutation methods often fails. Permutation-based DCA struggles to control type I errors, indicating poor performance in identifying true differences in biological correlations.

Keywords:
differential coexpressiondifferential correlationexchangeabilitypermutation test

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Comparing biological systems aids biological science progress.
  • Differential correlation analysis (DCA) identifies differing genomic feature relationships across conditions.

Purpose of the Study:

  • Examine limitations of permutation-based DCA.
  • Investigate poor performance instances of permutation-based DCA.

Main Methods:

  • Analysis of molecular changes under different conditions.
  • Utilizing permutation in DCA to loosen parametric assumptions.
  • Examining type I error control under the null hypothesis.

Main Results:

  • Permutation-based DCA violates the assumption of sample exchangeability under the null.
  • Permutation-based DCA exhibits poor performance in specific instances.
  • Permutation-based DCA often fails to control type I error.

Conclusions:

  • Permutation-based DCA has significant limitations.
  • The method's performance issues stem from violating null hypothesis assumptions.
  • Caution is advised when using permutation-based DCA for biological correlation analysis.