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.
Kustra
Rafal
R
Department of Public Health Sciences, University of Toronto, Health Sciences Building, Toronto, Ontario, Canada. r.kustra@utoronto.ca
Zagdański
Adam
A
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*