MOTIVATION: The analysis of a number of different genetic features like copy number (CN) variation, gene expression (GE) or loss of heterocygosity has considerably increased in recent years, as well as the number of available datasets. This is particularly due to the success of microarray technology. Thus, to understand mechanisms of disease pathogenesis on a molecular basis, e.g. in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important. In order to tackle this problem, we propose a new procedure for an integrated analysis of two different data types that searches for genes and genetic regions which for both inputs display strong equally directed deviations from the reference median. We employ this approach, based on a modified correlation coefficient and an explorative Wilcoxon test, to find DNA regions of such abnormalities in GE and CN (e.g. underexpressed genes accompanied by a loss of DNA material). RESULTS: In an application to acute myeloid leukemia, our procedure is able to identify various regions on different chromosomes with characteristic abnormalities in GE and CN data and shows a higher sensitivity to differences in abnormalities than standard approaches. While the results support various findings of previous studies, some new interesting DNA regions can be identified. In a simulation study, our procedure also shows more reliable results than standard approaches. AVAILABILITY: Code and data available as R packages edira and ediraAMLdata from http://www.statistik.tu-dortmund.de/~schaefer/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Motivation: Unsupervised clustering of microarray data may detect potentially important, but not obvious characteristics of samples, for instance subgroups of diagnoses with distinct gene profiles or systematic errors in experimentation. Results: Multidimensional clustering (mdclust) is a method, which identifies sets of sample clusters and associated genes. It applies iteratively two-means clustering and score-based gene selection. For any phenotype variable best matching sets of clusters can be selected. This provides a method to identify gene–phenotype associations, suited even for settings with a large number of phenotype variables. An optional model based discriminant step may reduce further the number of selected genes. Availability: R-code and supplemental information available from http://martin-dugas.de/mdclust/ Supplementary information: http://martin-dugas.de/mdclust/