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Cluster stability scores for microarray data in cancer studies

Overview of attention for article published in BMC Bioinformatics, September 2003
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Title
Cluster stability scores for microarray data in cancer studies
Published in
BMC Bioinformatics, September 2003
DOI 10.1186/1471-2105-4-36
Pubmed ID
Authors

Mark Smolkin, Debashis Ghosh

Abstract

A potential benefit of profiling of tissue samples using microarrays is the generation of molecular fingerprints that will define subtypes of disease. Hierarchical clustering has been the primary analytical tool used to define disease subtypes from microarray experiments in cancer settings. Assessing cluster reliability poses a major complication in analyzing output from clustering procedures. While most work has focused on estimating the number of clusters in a dataset, the question of stability of individual-level clusters has not been addressed. We address this problem by developing cluster stability scores using subsampling techniques. These scores exploit the redundancy in biologically discriminatory information on the chip. Our approach is generic and can be used with any clustering method. We propose procedures for calculating cluster stability scores for situations involving both known and unknown numbers of clusters. We also develop cluster-size adjusted stability scores. The method is illustrated by application to data three cancer studies; one involving childhood cancers, the second involving B-cell lymphoma, and the final is from a malignant melanoma study. Code implementing the proposed analytic method can be obtained at the second author's website.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
France 1 1%
United Kingdom 1 1%
Canada 1 1%
Japan 1 1%
United States 1 1%
Unknown 70 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 32%
Student > Ph. D. Student 18 24%
Professor > Associate Professor 7 9%
Student > Master 5 7%
Other 4 5%
Other 10 13%
Unknown 8 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 37%
Computer Science 16 21%
Medicine and Dentistry 5 7%
Mathematics 5 7%
Biochemistry, Genetics and Molecular Biology 3 4%
Other 8 11%
Unknown 11 14%