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Capiu

Clustering using a priori information via unsupervised decision trees is a method for extracting groups of samples in a microarray experiment. The result is directly interpretable as the method uses pre-defined classes of genes (such as those from Gene ontology or MapMan) to build a clustering tree with the informative gene classes as its nodes and groups of samples as its leaves.
general

R library for CAPIU analysis for microarray experiments. CAPIU is a novel approach for clustering samples (treatments, patients, condition etc) by using annotational information about the genes. The algorithm searches all pre-defined gene classes for classes that exhibit a strong clustering of the samples. These are then used to split the samples in two groups until no significant splits can be found. The result is visualized as a tree with gene classes as nodes and groups of samples as leaves. For questions, comments, bugs etc, please contact Henning Redestig.

examples

Examples are provided in the R documentation files.

dependencies

R [tested with v2.2.0 on FC4 GNU/Linux]
Following packages are also needed: Biobase, MASS, mclust, e1071, cluster, hu6800, ellipse, GO.
dot, which is part of the Graphviz package.

documentation

The manuscript "Integrating functional knowledge during sample clustering for microarray data using unsupervised decision trees" has been accepted for publication in the Biometrical Journal. The Supplementary material is available here.

downloads
program file: capiu_0.2.tar.gz
R documentation: capiu.pdf
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