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Related Concept Videos

Coefficient of Correlation01:12

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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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Wilcoxon Signed-Ranks Test for Median of Single Population01:14

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The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Generating weighted and thresholded gene coexpression networks using signed distance correlation.

Javier Pardo-Diaz1, Philip S Poole2, Mariano Beguerisse-Díaz3

  • 1Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.

Network Science (Cambridge University Press)
|October 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for building weighted gene coexpression networks using signed distance correlation. This approach enhances biological information capture and network stability compared to existing methods.

Keywords:
Gene expressioncorrelationrobustnessweighted networks

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Functional gene annotation is crucial for understanding biological systems, yet many genes remain unannotated.
  • Gene coexpression networks are valuable tools for inferring gene function and biological relationships.
  • Existing methods for constructing gene coexpression networks, such as using Pearson correlation, may lose information by converting continuous correlation values into unweighted networks.

Purpose of the Study:

  • To develop a principled method for constructing weighted gene coexpression networks.
  • To leverage signed distance correlation for a more informative network construction.
  • To compare the performance of signed distance correlation-based weighted networks against existing methods.

Main Methods:

  • Developed a method to construct weighted gene coexpression networks using signed distance correlation.
  • Weighted edges were assigned to gene pairs with correlation values exceeding a specified threshold.
  • Analyzed gene expression data from multiple organisms.

Main Results:

  • Networks constructed using signed distance correlation-based weighted approach are more stable.
  • These networks capture significantly more biological information than those derived from Pearson correlation.
  • Signed distance correlation-based weighted networks outperform unweighted networks derived from the same metric.

Conclusions:

  • Weighted gene coexpression networks built with signed distance correlation offer improved stability and biological insight.
  • This method provides a more comprehensive representation of gene relationships compared to traditional unweighted networks.
  • The approach is broadly applicable to network construction in various biological and non-biological domains.