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Construction and use of gene expression covariation matrix
Jérôme Hennetin1, Petri Pehkonen, Michel Bellis
1Centre de Recherches en Biochimie Macromoléculaire, CNRS, Montpellier, France. jerome.hennetin@tiscali.fr
BMC Bioinformatics
|July 15, 2009
Summary
This study introduces a novel method to calculate gene covariation using single-channel transcriptomic data, enabling robust analysis of gene expression patterns across multiple conditions. The approach generates comparable covariation matrices (CVM) and reveals conserved functional gene regions.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Massive transcriptomic profile analysis relies on calculating correlation coefficients to identify genes with similar or inverse expression patterns.
- Traditional methods are limited by technology: double-channel techniques use covariation correlation, while single-channel techniques use coexpression correlation.
- The benefits of using covariation for single-channel microarray analysis have not been previously explored.
Purpose of the Study:
- To adapt single-channel transcriptomic techniques for covariation measure generation, similar to double-channel methods.
- To introduce a new method for calculating both positive and negative correlation coefficients between genes.
- To analyze large transcriptomic datasets and identify conserved functional gene regions.
Main Methods:
- Single-channel data is transformed by comparing gene expression changes between conditions, classifying genes as increased (I), decreased (D), or not changed (N).
- This creates a symbolic string for each gene representing its expression profile across comparisons.
- Positive and negative covariation matrices (CVM) are constructed by calculating statistically significant correlation scores between these symbolic strings for gene pairs.
Main Results:
- The new method successfully generated distinct covariation matrices (CVM) with similar properties across four large datasets.
- These covariation networks were translated into 3D graphical representations.
- Probe set assignments within these networks were conserved across different chip set models and species (humans, mice, rats).
Conclusions:
- The developed method effectively enables covariation analysis on single-channel transcriptomic data.
- Clustering analysis of the covariation networks delineated six conserved functional gene regions.
- These conserved regions were further characterized using Gene Ontology information, highlighting their biological significance.
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