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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	BMC bioinformatics Ročník 10; číslo 1; s. 260
Hlavní autoři:	Kim, Eun-Youn, Kim, Seon-Young, Ashlock, Daniel, Nam, Dougu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 22.08.2009 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/1471-2105-10-260