title: Evaluation of normalization and pre-clustering issues in a novel clustering approach: global optimum search with enhanced positioning.
abstract: We study the effects on clustering quality by different normalization and pre-clustering techniques for a novel mixed-integer nonlinear optimization-based clustering algorithm, the Global Optimum Search with Enhanced Positioning (EP_GOS_Clust). These are important issues to be addressed. DNA microarray experiments are informative tools to elucidate gene regulatory networks. But in order for gene expression levels to be comparable across microarrays, normalization procedures have to be properly undertaken. The aim of pre-clustering is to use an adequate amount of discriminatory characteristics to form rough information profiles, so that data with similar features can be pre-grouped together and outliers deemed insignificant to the clustering process can be removed. Using experimental DNA microarray data from the yeast Saccharomyces Cerevisiae, we study the merits of pre-clustering genes based on distance/correlation comparisons and symbolic representations such as {+, o, -}. As a performance metric, we look at the intra- and inter-cluster error sums, two generic but intuitive measures of clustering quality. We also use publicly available Gene Ontology resources to assess the clusters' level of biological coherence. Our analysis indicates a significant effect by normalization and pre-clustering methods on the clustering results. Hence, the outcome of this study has significance in fine-tuning the EP_GOS_Clust clustering approach.
authors.affiliation: Department of Chemical Engineering, Princeton University, Princeton, NJ 08544, USA. mtan@princeton.edu
authors.forename: Meng P
authors.initials: MP
authors.lastname: Tan
authors.forename: James R
authors.initials: JR
authors.lastname: Broach
authors.forename: Christodoulos A
authors.initials: CA
authors.lastname: Floudas
mesh_terms: D000465:Algorithms; D016477:Artifacts; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods / Q000706:statistics & numerical data; D012313:RNA / Q000032:analysis; D012015:Reference Standards; D015203:Reproducibility of Results; D012107:Research Design / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D012680:Sensitivity and Specificity; D012984:Software