CGGA
Co-expressed Gene Groups Analysis
Description
Implementation of the CGGA algorithm for extracting bi-clusters of co-regulated genes from integrated gene expression data and gene annotations obtained from biological knowledge.
First, the gene rank hierarchy is constructed using SAM F-Scores and then, the CGGA algorithm is applied to group co-expressed genes.
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Reference
Co-expressed Gene Groups Analysis (CGGA): An automatic tool for the interpretation of microarray experiments, Ricardo Martinez, Nicolas Pasquier, Claude Pasquier, Martine Collard and Lucero Lopez-Perez, Journal of Integrative Bioinformatics, 3:2(1-12), 2006.
Experimental Results
These results were obtained from the integrated Derisi et al. dataset containing 2465 Yeast gene expression measurements for 7 biological conditions and gene annotations.
- Down-regulated genes ranks: F-Score statistics computed by SAM to obtain under-expressed gene scores and their rank.
- Up-regulated genes ranks: F-Score statistics computed by SAM to obtain over-expressed gene scores and their rank.
- Results down-regulated: Resulting groups of down-regulated genes obtained with CGGA.
- Results up-regulated: Resulting groups of up-regulated genes obtained with CGGA.
- Derisi et al. dataset annotations: Annotations for the Derisi et al. dataset.
- Summary of the results: Summary of experimental results obtained with CGGA from the Derisi et al. dataset.
Related Publications
Analyse des groupes de gènes co-exprimés : un outil automatique pour l'interprétation des expériences de biopuces, Ricardo Martinez, Nicolas Pasquier, Claude Pasquier, Martine Collard and Lucero Lopez-Perez, Revue des Nouvelles Technologies de l'Information, Classification : points de vue croisés, 2008.
Knowledge integration models for mining gene expression data, Ricardo Martinez, PhD Thesis, Université de Nice Sophia Antipolis, 2007.
Interpreting microarray experiments via co-expressed gene groups analysis, Ricardo Martinez, Nicolas Pasquier, Claude Pasquier and Lucero Lopez-Perez, Proceedings of the DS international conference on Discovery Science, pages 316-320, Springer-Verlag, 2006.
Analyse des groupes de gènes co-exprimés (AGGC) : un outil automatique pour l'interpretation des expériences de biopuces, Ricardo Martinez, Nicolas Pasquier, Martine Collard, Lucero Lopez-Perez and Claude Pasquier, Actes des XIIIème Rencontres de la Société Française de Classification, Université de Metz et LITA, 2006.