Data-fusion in clustering microarray data: balancing discovery and interpretability.
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.
Department of Public Health Sciences, University of Toronto, Health Sciences Building, Toronto, Ontario, Canada. r.kustra@utoronto.ca
Rafal
R
Kustra
Adam
A
Zagdański
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D003628:Database Management Systems; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D005810:Multigene Family; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*