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All radioactive nuclides emit high-energy particles or electromagnetic waves. When this radiation encounters living cells, it can cause heating, break chemical bonds, or ionize molecules. The most serious biological damage results when these radioactive emissions fragment or ionize molecules. For example, α and β particles emitted from nuclear decay reactions possess much higher energies than ordinary chemical bond energies. When these particles strike and penetrate matter, they...
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A test for detecting differential indirect trans effects between two groups of samples.

Nimisha Chaturvedi1,2, Renée X de Menezes1,2, Jelle J Goeman3

  • 1Afdeling Epidemiologie en Biostatistiek, Amsterdam Public Health Research Institute, Medische Faculteit (F-vleugel), VU Medisch Centrum, 1007 MB Amsterdam, The Netherlands.

Statistical Applications in Genetics and Molecular Biology
|July 31, 2018
PubMed
Summary

dNET is a new method to analyze how gene interactions change between sample groups, considering copy number variations. It accurately identifies differences in gene networks, even with complex genetic effects.

Keywords:
group testinghigh dimensional datamultivariate analysisnetwork analysis

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Copy number aberrations influence gene expression through cis and trans effects.
  • Understanding gene-gene interactions mediated by copy number is crucial for pathway analysis.
  • Existing methods may not effectively capture differential network topologies between sample groups.

Purpose of the Study:

  • To introduce dNET, a novel method for analyzing differential gene-gene interactions influenced by copy number variations.
  • To assess the performance of dNET in detecting network differences across sample groups.
  • To identify cancer pathways with altered copy number-mediated interactions in advanced stages.

Main Methods:

  • dNET employs ridge regression to model gene expression networks, incorporating gene dosage and expression levels.
  • It estimates interaction parameters across all samples and tests for overall differences between two groups.
  • The method generates a single p-value to indicate differential network topology.

Main Results:

  • Simulation studies demonstrate dNET's high accuracy and low false positive rate in detecting differential network nodes.
  • dNET effectively handles scenarios with differential cis effects.
  • Application to TCGA cancer data revealed distinct copy number-mediated interactions in advanced cancer stages (3+).

Conclusions:

  • dNET provides a robust approach for identifying differential gene-gene interactions driven by copy number aberrations.
  • The method is accurate and reliable, even with complex genetic data.
  • dNET facilitates the discovery of pathway alterations in cancer progression.