Title |
A systematic comparison of genome-scale clustering algorithms
|
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Published in |
BMC Bioinformatics, June 2012
|
DOI | 10.1186/1471-2105-13-s10-s7 |
Pubmed ID | |
Authors |
Jeremy J Jay, John D Eblen, Yun Zhang, Mikael Benson, Andy D Perkins, Arnold M Saxton, Brynn H Voy, Elissa J Chesler, Michael A Langston |
Abstract |
A wealth of clustering algorithms has been applied to gene co-expression experiments. These algorithms cover a broad range of approaches, from conventional techniques such as k-means and hierarchical clustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks (WGCNA) and paraclique. Comparison of these methods to evaluate their relative effectiveness provides guidance to algorithm selection, development and implementation. Most prior work on comparative clustering evaluation has focused on parametric methods. Graph theoretical methods are recent additions to the tool set for the global analysis and decomposition of microarray co-expression matrices that have not generally been included in earlier methodological comparisons. In the present study, a variety of parametric and graph theoretical clustering algorithms are compared using well-characterized transcriptomic data at a genome scale from Saccharomyces cerevisiae. |
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Geographical breakdown
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Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
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United States | 2 | 3% |
Cuba | 1 | 2% |
Unknown | 59 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 17 | 27% |
Student > Ph. D. Student | 12 | 19% |
Student > Master | 9 | 15% |
Other | 4 | 6% |
Student > Doctoral Student | 3 | 5% |
Other | 11 | 18% |
Unknown | 6 | 10% |
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Biochemistry, Genetics and Molecular Biology | 11 | 18% |
Computer Science | 8 | 13% |
Engineering | 4 | 6% |
Neuroscience | 2 | 3% |
Other | 8 | 13% |
Unknown | 8 | 13% |