Kernel hierarchical gene clustering from microarray expression data

• Published in: Bioinformatics 2003-11, Vol.19 (16), p.2097-2104
• Main Authors: Qin, Jie, Lewis, Darrin P., Noble, William Stafford
• Format: Article
• Language: English
• Subjects: Algorithms; Animals; Artificial Intelligence; Biological and medical sciences; Cell Cycle - genetics; Cluster Analysis; DNA microarrays; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Mice; Neoplasms - classification; Neoplasms - genetics; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Sequence Analysis, DNA - methods; Yeasts - genetics
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btg288

Motivation: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchical clustering can be applied to expression profiles of genes across multiple experiments, identifying groups of genes that share similiar expression profiles. Previous work using the support vector machine supervised learning algorithm with microarray data suggests that higher-order features, such as pairwise and tertiary correlations across multiple experiments, may provide significant benefit in learning to recognize classes of co-expressed genes. Results: We describe a generalization of the hierarchical clustering algorithm that efficiently incorporates these higher-order features by using a kernel function to map the data into a high-dimensional feature space. We then evaluate the utility of the kernel hierarchical clustering algorithm using both internal and external validation. The experiments demonstrate that the kernel representation itself is insufficient to provide improved clustering performance. We conclude that mapping gene expression data into a high-dimensional feature space is only a good idea when combined with a learning algorithm, such as the support vector machine that does not suffer from the curse of dimensionality. Availability: Supplementary data at www.cs.columbia.edu/compbio/hiclust. Software source code available by request.