MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus...

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Published in:BMC bioinformatics Vol. 10; no. 1; p. 260
Main Authors: Kim, Eun-Youn, Kim, Seon-Young, Ashlock, Daniel, Nam, Dougu
Format: Journal Article
Language:English
Published: London BioMed Central 22.08.2009
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Bioinformatics
Biomedical and Life Sciences
Classification
Cluster Analysis
Computational Biology - methods
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Databases, Genetic
DNA microarrays
Entropy
Gene expression
Genetic algorithms
Life Sciences
Methodology
Methodology Article
Methods
Microarrays
Oligonucleotide Array Sequence Analysis - methods
Pattern Recognition, Automated
Software
Studies
Canada
South Korea
Geometric Complexity
Ensemble Cluster
Cluster Strategy
Adjusted Rand Index
Cluster Structure
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K , for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k -means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k -means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-10-260