Difference between revisions of "Cluster analysis by K-means (analysis)"
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Revision as of 11:47, 6 May 2013
- Analysis title
Cluster analysis by K-means
- Provider
- Institute of Systems Biology
- Plugin
- ru.biosoft.analysis (Common methods of data analysis plug-in)
Contents |
Goal:
Genes are grouped into clusters so that those in one cluster exhibit maximal similarity, whereas those of different clusters are maximally dissimilar.
Input:
A table of genes or probes with their expression values or fold change calculated. Depending on the algorithm, input of certain parameters is required.
Output:
A table with the same genes grouped into clusters.
Parameters:
- Experiment data - experimental data for analysis.
- Table - a table with experimental data stored in repository.
- Columns - the columns from the table which should be taken for the clustering analysis.
- Cluster algorithm - the version of the K-means algorithm to be applied [1-4].
- Cluster number - the number of clusters into which the input data will be divided.
- Output table - name and path in the repository under which the result table will be saved. If a table with the specified name and path already exists, it will be overwritten.
Further details:
The clustering is done with the K-means algorithm as implemented in the R package (http://www.r-project.org/).
References:
- Forgy, E. W. (1965) Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics 21, 768–769.
- Hartigan, J. A. and Wong, M. A. (1979). A K-means clustering algorithm. Applied Statistics 28, 100–108.
- Lloyd, S. P. (1957, 1982) Least squares quantization in PCM. Technical Note, Bell Laboratories. Published in 1982 in IEEE Transactions on Information Theory 28, 128–137.
- MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281–297. Berkeley, CA: University of California Press.
