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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Learning dysregulated pathways in cancers from differential variability analysis.

Bahman Afsari1, Donald Geman2, Elana J Fertig3

  • 1Postdoctoral Fellow, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.

Cancer Informatics
|November 14, 2014
PubMed
Summary
This summary is machine-generated.

Analyzing gene expression data for cancer pathways requires advanced methods beyond individual gene analysis. Differential variability analysis, including the new expression variation analysis (EVA) algorithm, offers a more comprehensive approach to inferring pathway activity.

Keywords:
gene expressiongene set analysismultivariate analysisvariability analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Genomics

Background:

  • Gene set analysis is crucial for understanding cancer initiation and progression by identifying signaling pathway activity.
  • Existing methods like over-representation and enrichment analysis effectively infer differentially regulated pathways but overlook complex gene interactions and expression variations.
  • Multivariate analysis of gene expression patterns is essential for accurate pathway inference in cancer.

Purpose of the Study:

  • To review existing methodologies and software for multivariate variability analysis of pathways.
  • To introduce a novel, computationally efficient algorithm, expression variation analysis (EVA).
  • To compare EVA with existing methods like Differential Rank Conservation (DIRAC) and enrichment analysis.

Main Methods:

  • Review of multivariate variability analysis methodologies for gene sets.
  • Implementation of EVA and DIRAC algorithms in the open-source R package, gene set regulation (GSReg).
  • Comparative analysis of pathway inference results from EVA, DIRAC, and enrichment analysis.

Main Results:

  • The expression variation analysis (EVA) algorithm infers pathways comparable to Differential Rank Conservation (DIRAC).
  • EVA achieves similar pathway inference results as DIRAC with significantly reduced computational costs.
  • EVA identifies distinct dysregulated pathways compared to traditional enrichment analysis.

Conclusions:

  • Differential variability analysis provides a more robust approach to pathway inference in cancer genomics.
  • The EVA algorithm offers a computationally efficient and effective tool for identifying dysregulated pathways.
  • The gene set regulation (GSReg) R package facilitates the application of advanced pathway analysis methods.