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Generalized correlation measure using count statistics for gene expression data with ordered samples.

Y X Rachel Wang1, Ke Liu2, Elizabeth Theusch3

  • 1School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia.

Bioinformatics (Oxford, England)
|October 18, 2017
PubMed
Summary

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This summary is machine-generated.

We developed a novel count statistic to detect complex gene expression associations, even with time lags or across species. This method aids in inferring gene regulatory networks and aligning biological data effectively.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Inferring gene regulatory interactions requires analyzing gene expression patterns across conditions or time.
  • Standard correlation methods struggle with time-lagged associations and sample-specific interactions.
  • Comparing gene expression profiles across species or misaligned datasets presents challenges.

Purpose of the Study:

  • To introduce a new count statistic for measuring associations between ordered gene expression profiles.
  • To address limitations of traditional correlation measures in detecting complex association patterns.
  • To enable robust comparisons of gene expression data, even when samples are not directly aligned.

Main Methods:

  • Developed a novel count statistic to identify local correlations in subsequences of ordered gene expression profiles.

Related Experiment Videos

  • Applied the statistic to cross-species gene expression data for developmental stage alignment.
  • Utilized the statistic on gene expression profiles from distinct phenotypic conditions ordered by phenotypic values.

    • Main Results:

    • The proposed statistic effectively measures gene expression association between species and aligns developmental stages.
    • It facilitates the construction of correspondences between gene association networks across different phenotypes.
    • Theoretical analysis provides asymptotic distributions, and simulations confirm the statistic's power.

    Conclusions:

    • The novel count statistic offers a powerful and efficient tool for detecting complex gene expression associations.
    • It enhances the ability to infer gene regulatory interactions and build cross-dataset network correspondences.
    • The method is applicable to diverse biological scenarios, including cross-species and cross-phenotype analyses.