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Recursive expectation-maximization clustering: a method for identifying buffering mechanisms composed of phenomic
Jingyu Guo1, Dehua Tian, Brett A McKinney
1Department of Genetics, University of Alabama at Birmingham, Birmingham, Alabama 35294, USA.
Chaos (Woodbury, N.Y.)
|July 2, 2010
Summary
We developed recursive expectation-maximization clustering (REMc) to analyze complex gene interactions in yeast, identifying phenomic modules that maintain cellular homeostasis and regulate phenotypic expression.
Area of Science:
- Systems Biology
- Genetics
- Phenomics
Background:
- Understanding gene and environmental interactions is crucial for deciphering complex biological phenomena and human diseases.
- Phenomics, the integrative analysis of gene contributions to phenotypic variation, requires a systems biology approach.
- Studying gene interactions in model organisms like Saccharomyces cerevisiae (S. cerevisiae) is essential due to the complexity in humans.
Purpose of the Study:
- To develop and validate a novel computational method for mining quantitative genetic interaction data.
- To identify phenomic modules that reveal gene-gene and gene-environment interactions related to buffering mechanisms.
- To enhance the understanding of how genes and pathways maintain physiological homeostasis.
Main Methods:
- Quantitative high-throughput cellular phenotyping (Q-HTCP) was employed for high-resolution measurement of gene interactions.
- A new data mining approach, recursive expectation-maximization clustering (REMc), was developed to analyze Q-HTCP data.
- The method was tested using 297 S. cerevisiae gene deletion strains challenged with various growth-inhibitory drugs, and performance was compared to hierarchical and K-means clustering.
Main Results:
- REMc successfully identified phenomic modules, defined as sets of genes with similar interaction patterns across perturbations.
- The method provides objective assessment of cluster quality using log-likelihood values and biological relevance via gene ontology information divergence z-score (GOid_z).
- REMc demonstrated distinct efficiencies in mining Q-HTCP data compared to traditional clustering methods.
Conclusions:
- REMc is an effective tool for discovering phenomic modules from quantitative genetic interaction data.
- These phenomic modules are indicative of buffering mechanisms crucial for cellular homeostasis and phenotypic regulation.
- The developed approach advances the systems biology view of gene interactions and their role in maintaining biological stability.
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