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NCR-PCOPGene: An Exploratory Tool for Analysis of Sample-Classes Effect on Gene-Expression Relationships
Juan Cedano1, Mario Huerta, Enrique Querol
1Departament de Bioquímica i Biología Molecular, Institut de Biotecnologia i Biomedicina, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain.
Advances in Bioinformatics
|November 19, 2009
Summary
This study introduces PCOPGene, a tool for analyzing microarray data to uncover complex gene expression relationships. It helps identify cellular states and involved genes, even for non-linear correlations in large datasets.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Microarray technology generates powerful but expensive data, necessitating methods to maximize value extraction.
- Existing tools enable hypothesis testing and model formulation from microarray data.
- The NCRPCOPGene project focuses on understanding gene expression relationships.
Purpose of the Study:
- To study relationships among gene expressions under various conditions.
- To classify these conditions and their effects on gene expression.
- To provide tools for analyzing complex gene expression patterns.
Main Methods:
- Development of a web application for defining sample classes based on prior knowledge or observed effects.
- Grouping microarray experiments using biological, statistical, or other information.
- Linking genes with non-linear or non-continuous expression relationships.
Main Results:
- The PCOPGene web application facilitates the definition of sample classes.
- It enables the addition of biological context to gene expression relationships.
- The system links genes with correlations not detectable by linear or non-linear methods.
Conclusions:
- PCOPGene tools are highly effective for microarrays with extensive sample series.
- The application aids in flexibly identifying cellular states and associated genes.
- It leverages the system's capability to relate gene expressions, including non-continuous patterns.
