mdclust—exploratory microarray analysis by multidimensional clustering
| Autor: | P. Dirschedl, Sylvia Merk, Martin Dugas, Susanne Breit |
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| Rok vydání: | 2004 |
| Předmět: |
Statistics and Probability
Clustering high-dimensional data Computer science Sample (statistics) computer.software_genre Sensitivity and Specificity Biochemistry Pattern Recognition Automated Computer Graphics Cluster Analysis Humans Cluster analysis Molecular Biology Oligonucleotide Array Sequence Analysis Multidimensional analysis Leukemia Microarray analysis techniques Gene Expression Profiling Reproducibility of Results Sequence Analysis DNA Computer Science Applications Computational Mathematics Variable (computer science) Computational Theory and Mathematics Data mining Sequence Alignment computer Software |
| Zdroj: | Bioinformatics. 20:931-936 |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/bth009 |
| Popis: | 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/ |
| Databáze: | OpenAIRE |
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