title: Inferential clustering approach for microarray experiments with replicated measurements.
abstract: Cluster analysis has proven to be a useful tool for investigating the association structure among genes in a microarray data set. There is a rich literature on cluster analysis and various techniques have been developed. Such analyses heavily depend on an appropriate (dis)similarity measure. In this paper, we introduce a general clustering approach based on the confidence interval inferential methodology, which is applied to gene expression data of microarray experiments. Emphasis is placed on data with low replication (three or five replicates). The proposed method makes more efficient use of the measured data and avoids the subjective choice of a dissimilarity measure. This new methodology, when applied to real data, provides an easy-to-use bioinformatics solution for the cluster analysis of microarray experiments with replicates (see the Appendix). Even though the method is presented under the framework of microarray experiments, it is a general algorithm that can be used to identify clusters in any situation. The method's performance is evaluated using simulated and publicly available data set. Our results also clearly show that our method is not an extension of the conventional clustering method based on correlation or euclidean distance.
authors.affiliation: Statistics Department, Barcelona University, Avda Diagonal 645, 08028 BCN, Spain. msalicru@ub.edu
authors.forename: Miquel
authors.initials: M
authors.lastname: Salicrú
authors.forename: Sergi
authors.initials: S
authors.lastname: Vives
authors.forename: Tian
authors.initials: T
authors.lastname: Zheng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D011381:Programming Languages; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods