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Inferring gene networks from discrete expression data.

Lin Zhang1, Bani K Mallick

  • 1Texas A&M University, College Station, TX 77843, USA.

Biostatistics (Oxford, England)
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new discrete graphical model for gene network inference from gene expression data. The method enhances the analysis of RNA sequencing and serial analysis of gene expression experiments.

Keywords:
Discrete graphical modelGene expression networkGene ontologyRNA-SeqSAGE

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene network modeling is crucial for understanding biological pathways and identifying therapeutic targets.
  • Existing methods primarily use Gaussian graphical models for continuous data.
  • Discrete gene expression data from RNA sequencing and SAGE experiments require novel modeling approaches.

Purpose of the Study:

  • To extend gene network modeling to discrete gene expression data.
  • To develop a robust method for inferring gene networks from count-based expression data.
  • To incorporate prior biological knowledge into network inference.

Main Methods:

  • Proposed a generalized linear model for discrete gene expression data.
  • Assumed log ratios of mean expression levels follow a Gaussian distribution.
  • Utilized decomposable graphs and hyper-inverse Wishart priors for graph structure selection.
  • Incorporated Gene Ontology information for prior network models.

Main Results:

  • Developed and validated a discrete graphical model for gene network inference.
  • Demonstrated the model's performance through simulation studies.
  • Applied the method to two real-world gene expression datasets.

Conclusions:

  • The proposed discrete graphical model effectively infers gene networks from count-based expression data.
  • This approach expands network modeling capabilities for RNA sequencing and SAGE data.
  • Integration of prior biological information improves network inference accuracy.