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Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially

Yiming Zuo1,2,3, Yi Cui2, Guoqiang Yu1

  • 1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, 22203, VA, USA.

BMC Bioinformatics
|February 12, 2017
PubMed
Summary

This study introduces dwgLASSO, a network-based method for differential gene expression analysis. It improves biomarker discovery by integrating gene expression data with biological networks, outperforming traditional methods in cancer studies.

Keywords:
Gaussian graphical modelNetwork-based differential gene expression analysisPrior biological knowledgeWeighted graphical LASSO

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Conventional differential gene expression analysis often overlooks gene interactions.
  • Network-based approaches are crucial for studying gene interactions and rewiring in disease states.
  • Prior biological knowledge, such as protein-protein interactions, can enhance network inference.

Purpose of the Study:

  • To develop a novel network-based differential gene expression analysis algorithm (dwgLASSO).
  • To integrate prior biological knowledge into network inference for improved biomarker candidate selection.
  • To assess the performance of dwgLASSO in identifying biologically meaningful genes for cancer biomarker studies.

Main Methods:

  • Application of weighted graphical LASSO (wgLASSO) for biological network inference.
  • Development of differentially weighted graphical LASSO (dwgLASSO) to build group-specific networks.
  • Network-based differential gene expression analysis considering topological differences between groups.

Main Results:

  • wgLASSO demonstrated superior performance in building biologically relevant networks compared to purely data-driven models.
  • dwgLASSO significantly improved survival time prediction in breast cancer datasets.
  • dwgLASSO showed improved sensitivity, specificity, and AUC in hepatocellular carcinoma (HCC) analysis compared to conventional methods.
  • Identified genes like UBE2S, SALL2, XBP1, and KIAA0922 are highly relevant in breast cancer biomarker discovery.

Conclusions:

  • The dwgLASSO algorithm enhances differential gene expression analysis by integrating gene expression and network topology information.
  • Incorporating prior biological knowledge aids in identifying biologically meaningful genes for cancer biomarker discovery.
  • Network-based approaches offer advantages over conventional methods for complex biological data analysis.