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- MATTHIAS E. FUTSCHIK
- Institute of Theoretical Biology, Humboldt-University, Invalidenstr. 43, 10115 Berlin, Germany
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- BRONWYN CARLISLE
- Department of Biochemistry, University of Otago, PO Box 56, Dunedin, New Zealand
書誌事項
- 公開日
- 2005-08
- DOI
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- 10.1142/s0219720005001375
- 公開者
- World Scientific Pub Co Pte Ltd
この論文をさがす
説明
<jats:p>Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise.</jats:p><jats:p>To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available.</jats:p>
収録刊行物
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- Journal of Bioinformatics and Computational Biology
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Journal of Bioinformatics and Computational Biology 03 (04), 965-988, 2005-08
World Scientific Pub Co Pte Ltd
