title: ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles.
abstract: One of the significant challenges in gene expression analysis is to find unknown subtypes of several diseases at the molecular levels. This task can be addressed by grouping gene expression patterns of the collected samples on the basis of a large number of genes. Application of commonly used clustering methods to such a dataset however are likely to fail owing to over-learning, because the number of samples to be grouped is much smaller than the data dimension which is equal to the number of genes involved in the dataset. To overcome such difficulty, we developed a novel model-based clustering method, referred to as the mixed factors analysis. The ArrayCluster is a freely available software to perform the mixed factors analysis. It provides us some analytic tools for clustering DNA microarray experiments, data visualization and an automatic detector for module transcriptional of genes that are relevant to the calibrated molecular subtypes and so on.
authors.affiliation: Human Genome Center, Institute of Medical Science, University of Tokyo 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. yoshidar@ims.u-tokyo.ac.jp
authors.forename: Ryo
authors.initials: R
authors.lastname: Yoshida
authors.forename: Tomoyuki
authors.initials: T
authors.lastname: Higuchi
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D015233:Models, Statistical; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011381:Programming Languages; D012984:Software