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Diametrical clustering for identifying anti-correlated gene clusters
Inderjit S Dhillon1, Edward M Marcotte, Usman Roshan
1Department of Computer Sciences, Institute for Cellular and Molecular Medicine, University of Texas, Austin, TX 78712, USA.
Bioinformatics (Oxford, England)
|September 12, 2003
Summary
This study introduces a novel diametrical clustering algorithm to identify functionally similar genes with anti-correlated expression patterns. The method reveals opposing cellular pathways and inverse transcriptional regulation, enhancing gene function prediction.
Area of Science:
- Computational Biology
- Bioinformatics
- Systems Biology
Background:
- Gene expression clustering aids in predicting gene function.
- Traditional methods focus on positively correlated gene expression.
- Functionally similar genes can exhibit anti-correlated expression patterns.
Purpose of the Study:
- To develop a novel diametrical clustering algorithm.
- To identify gene clusters with anti-correlated expression patterns.
- To discover opposing cellular pathways and regulatory mechanisms.
Main Methods:
- Iterative gene re-partitioning.
- Computation of dominant singular vectors for gene clusters.
- Development of a diametrical clustering algorithm.
Main Results:
- The algorithm effectively identifies diametrical (anti-correlated) gene clusters.
- Application to yeast cell cycle and fibroblast data revealed opposed cellular pathways.
- Evidence of inverse transcriptional regulation between cellular systems was found.
Conclusions:
- Diametrical clustering is a valuable approach for uncovering functional gene relationships.
- The method can identify opposing biological pathways and regulatory networks.
- This enhances the understanding of gene function and cellular processes.