A ground truth based comparative study on clustering of gene expression data

Yitan Zhu, Zuyi Wang, David J. Miller, Robert Clarke, Jianhua Xuan, Eric P. Hoffman, Yue Wang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Given the variety of available clustering methods for gene expression data analysis, it is important to develop an appropriate and rigorous validation scheme to assess the performance and limitations of the most widely used clustering algorithms. In this paper, we present a ground truth based comparative study on the functionality, accuracy, and stability of five data clustering methods, namely hierarchical clustering, K-means clustering, self-organizing maps, standard finite normal mixture fitting, and a caBIG™ toolkit (Visual Statistical Data Analyzer - VISDA), tested on sample clustering of seven published microarray gene expression datasets and one synthetic dataset. We examined the performance of these algorithms in both data-sufficient and data-insufficient cases using quantitative performance measures, including cluster number detection accuracy and mean and standard deviation of partition accuracy. The experimental results showed that VISDA, an interactive coarse-to-fine maximum likelihood fitting algorithm, is a solid performer on most of the datasets, while K-means clustering and self-organizing maps optimized by the mean squared compactness criterion generally produce more stable solutions than the other methods.

Original languageEnglish (US)
Pages (from-to)3839-3849
Number of pages11
JournalFrontiers in Bioscience
Volume13
Issue number10
DOIs
StatePublished - 2008
Externally publishedYes

Keywords

  • Clustering evaluation
  • Comparative study
  • Gene expression data
  • Sample clustering

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