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Updated: Aug 7, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Linking co-expression modules with phenotypes.

Rakesh Kumar1, Krishna Kumar Ojha1, Harlokesh Narayan Yadav2

  • 1Department of Bioinformatics, Central University of South Bihar, Gaya, Bihar 824236, India.

Bioinformation
|March 13, 2023
PubMed
Summary
This summary is machine-generated.

Analysis of Rank (AOR) offers a novel method to link gene co-expression modules with clinical traits, minimizing information loss. This approach successfully identified gene modules associated with diabetes status in muscle tissue.

Keywords:
Analysis of rankco-expressiongene expressiongene modulenetwork

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Quantifying associations between gene co-expression modules and clinical traits often involves dimensionality reduction.
  • Traditional dimensionality reduction methods can lead to significant information loss.
  • The extent of information loss correlates with the variance captured by the reduced one-dimensional (1D) vector.

Purpose of the Study:

  • To introduce and evaluate a novel method, Analysis of Rank (AOR), for assessing the association between co-expression modules and clinical traits.
  • To address the information loss issue inherent in traditional dimensionality reduction techniques.

Main Methods:

  • The Analysis of Rank (AOR) method was developed to assess associations between gene co-expression modules and clinical traits.
  • AOR can utilize binary clinical trait labels or continuous traits dichotomized around the median.
  • The method was applied to muscle gene expression data.

Main Results:

  • The AOR method was successfully applied to gene expression profiles.
  • Significant associations were identified between specific gene modules and diabetes status.
  • This demonstrates the efficacy of AOR in capturing relevant biological associations.

Conclusions:

  • The Analysis of Rank (AOR) provides an effective alternative for quantifying module-trait associations, mitigating information loss.
  • AOR is a valuable tool for identifying biologically relevant gene modules linked to clinical phenotypes, such as diabetes.
  • This method has potential applications in various fields of biomedical research involving gene expression analysis.