Title :
Data-Fusion in Clustering Microarray Data: Balancing Discovery and Interpretability
Author :
Rafal Kustra;Adam Zagdanski
Author_Institution :
Dept. of Public Health Sci., Univ. of Toronto, Toronto, ON, Canada
Abstract :
While clustering genes remains one of the most popular exploratory tools for expression data, it often results in a highly variable and biologically uninformative clusters. This paper explores a data fusion approach to clustering microarray data. Our method, which combined expression data and gene ontology (GO)-derived information, is applied on a real data set to perform genome-wide clustering. A set of novel tools is proposed to validate the clustering results and pick a fair value of infusion coefficient. These tools measure stability, biological relevance, and distance from the expression-only clustering solution. Our results indicate that a data-fusion clustering leads to more stable, biologically relevant clusters that are still representative of the experimental data.
Keywords :
"Bioinformatics","Genomics","Ontologies","Stability","Databases","Information analysis","Turning","Signal to noise ratio","Information technology","Genetics"
Journal_Title :
IEEE/ACM Transactions on Computational Biology and Bioinformatics
DOI :
10.1109/TCBB.2007.70267