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A sequential clustering algorithm with applications to gene expression data

Title
A sequential clustering algorithm with applications to gene expression data
Authors
Song J.Nicolae D.L.
Ewha Authors
송종우
SCOPUS Author ID
송종우scopus
Issue Date
2009
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
vol. 38, no. 2, pp. 175 - 184
Indexed
SCIE; SCOPUS; KCI WOS scopus
Abstract
Clustering algorithms are used in the analysis of gene expression data to identify groups of genes with similar expression patterns. These algorithms group genes with respect to a predefined dissimilarity measure without using any prior classification of the data. Most of the clustering algorithms require the number of clusters as input, and all the objects in the dataset are usually assigned to one of the clusters. We propose a clustering algorithm that finds clusters sequentially, and allows for sporadic objects, so there are objects that are not assigned to any cluster. The proposed sequential clustering algorithm has two steps. First it finds candidates for centers of clusters. Multiple candidates are used to make the search for clusters more efficient. Secondly, it conducts a local search around the candidate centers to find the set of objects that defines a cluster. The candidate clusters are compared using a predefined score, the best cluster is removed from data, and the procedure is repeated. We investigate the performance of this algorithm using simulated data and we apply this method to analyze gene expression profiles in a study on the plasticity of the dendritic cells. © 2008 The Korean Statistical Society.
DOI
10.1016/j.jkss.2008.09.006
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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