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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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PathVar: analysis of gene and protein expression variance in cellular pathways using microarray data.

Enrico Glaab1, Reinhard Schneider

  • 1Structural and Computational Biology Unit, EMBL, Meyerhofstrasse 1, 69117, Heidelberg, Germany. enrico.glaab@uni.lu

Bioinformatics (Oxford, England)
|November 30, 2011
PubMed
Summary
This summary is machine-generated.

PathVar identifies pathway deregulation by analyzing gene expression variance, offering new insights beyond average expression. This tool aids in discovering cellular pathway alterations and classifying samples using machine learning.

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Area of Science:

  • Functional genomics
  • Bioinformatics
  • Systems biology

Background:

  • Differential expression analysis is crucial for functional genomics.
  • Current methods focus on average expression, missing differential variance.
  • Pathway deregulation is common in gene and protein expression data.

Purpose of the Study:

  • Introduce PathVar, a novel web application for analyzing functional genomics data.
  • Detect differential variance in gene/protein expression within cellular pathways.
  • Identify and exploit pathway deregulation patterns for sample classification.

Main Methods:

  • Developed PathVar, a web application for pathway analysis.
  • Ranks gene/protein sets based on within-pathway expression variance differences.
  • Integrates machine learning for sample clustering and classification.

Main Results:

  • PathVar detects differential variance in pathway expression levels.
  • Identifies novel pathway deregulation patterns.
  • Enables sample classification based on pathway alterations.

Conclusions:

  • PathVar offers a new approach to functional genomics data analysis by focusing on expression variance.
  • The tool reveals pathway deregulation patterns missed by traditional methods.
  • PathVar facilitates sample classification and deeper biological insights.