Title |
Reproducible Clusters from Microarray Research: Whither?
|
---|---|
Published in |
BMC Bioinformatics, July 2005
|
DOI | 10.1186/1471-2105-6-s2-s10 |
Pubmed ID | |
Authors |
Nikhil R Garge, Grier P Page, Alan P Sprague, Bernard S Gorman, David B Allison |
Abstract |
In cluster analysis, the validity of specific solutions, algorithms, and procedures present significant challenges because there is no null hypothesis to test and no 'right answer'. It has been noted that a replicable classification is not necessarily a useful one, but a useful one that characterizes some aspect of the population must be replicable. By replicable we mean reproducible across multiple samplings from the same population. Methodologists have suggested that the validity of clustering methods should be based on classifications that yield reproducible findings beyond chance levels. We used this approach to determine the performance of commonly used clustering algorithms and the degree of replicability achieved using several microarray datasets. |
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Geographical breakdown
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Australia | 1 | 2% |
France | 1 | 2% |
Brazil | 1 | 2% |
Poland | 1 | 2% |
Unknown | 53 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 25 | 40% |
Student > Ph. D. Student | 14 | 23% |
Professor > Associate Professor | 6 | 10% |
Professor | 5 | 8% |
Student > Master | 3 | 5% |
Other | 5 | 8% |
Unknown | 4 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 23 | 37% |
Computer Science | 11 | 18% |
Medicine and Dentistry | 7 | 11% |
Economics, Econometrics and Finance | 3 | 5% |
Engineering | 3 | 5% |
Other | 8 | 13% |
Unknown | 7 | 11% |