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Testing for association between RNA-Seq and high-dimensional data.

Armin Rauschenberger1, Marianne A Jonker2, Mark A van de Wiel3,4

  • 1Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, 1007, MB, The Netherlands. a.rauschenberger@vumc.nl.

BMC Bioinformatics
|March 9, 2016
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Summary
This summary is machine-generated.

This study introduces a novel statistical test to analyze RNA sequencing and genomic data, overcoming challenges of variability and high dimensionality for better gene expression insights.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Analyzing RNA sequencing (RNA-Seq) data alongside genomic data presents significant challenges.
  • High variability in RNA-Seq and the high dimensionality of genomic datasets complicate association testing.

Purpose of the Study:

  • To develop a robust statistical method for testing associations between RNA-Seq and genomic data.
  • To address the inherent difficulties posed by data variability and dimensionality.

Main Methods:

  • Utilizes a negative binomial distribution to model RNA-Seq count data.
  • Employs a random-effects model to account for heterogeneity.
  • Develops an omnibus test analogous to regression analysis with overdispersed outcomes and more predictors than samples.

Main Results:

  • The proposed omnibus test effectively handles high variability and dimensionality in genomic datasets.
  • The method is conceptualized as a test of overall significance in a regression framework.

Conclusions:

  • The developed test accurately detects genetic and epigenetic alterations influencing gene expression.
  • Enables the investigation of complex gene expression regulatory mechanisms.
  • An R package, globalSeq, is available for implementation.