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On the (lack of) robustness of gene expression data clustering
University of Skövde, School of Humanities and Informatics.
University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Humanities and Informatics.
2004 (English)In: WSEAS Transactions on Biology and Biomedicine, ISSN 1109-9518, Vol. 1, no 2, 198-204 p.Article in journal (Refereed) Published
Abstract [en]

We assess the robustness of partitional clustering algorithms applied to gene expression data. A number of clusterings are made with identical parameter settings and input data using SOM and  k-means algorithms, which both rely on random initialisation and may produce different clusterings with different seeds. We define a reproducibility index and use it to assess the algorithms. The index is based on the number of pairs of genes consistently clustered together in different clusterings. The effect of noise applied to the original data is also studied. Our results show a lack of robustness for both classes of algorithms, with slightly higher reproducibility for SOM than for k-means.

Place, publisher, year, edition, pages
World Scientific and Engineering Academy and Society, 2004. Vol. 1, no 2, 198-204 p.
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:his:diva-2334OAI: oai:DiVA.org:his-2334DiVA: diva2:114132
Available from: 2008-12-19 Created: 2008-11-06 Last updated: 2014-08-19Bibliographically approved

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Gamalielsson, JonasOlsson, Björn
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf