Bioinformatics Vol. 19 no. 16 2003
pages 2097-2104
© 2003 Oxford University Press
Kernel hierarchical gene clustering from microarray expression data
1 Columbia Genome Center, Columbia University, 1150 St. Nicholas Avenue, New York, NY 10032, USA, 2 Department of Computer Science, Columbia University, 1214 Amsterdam Avenue, New York, NY 10027, USA and 3 Department of Genome Sciences, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195, USA
Received on July 13, 2002
; revised on December 20, 2002
; accepted on May 8, 2003
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.
Contact: jq22{at}columbia.edu
* To whom correspondence should be addressed.
Formerly William Noble Grundy, see www.gs.washington.edu/~noble/name-change.html
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  A. Bhattacharya and R. K. De Divisive Correlation Clustering Algorithm (DCCA) for grouping of genes: detecting varying patterns in expression profiles Bioinformatics, June 1, 2008; 24(11): 1359 - 1366. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D.-W. Kim, K.-Y. Lee, K. H. Lee, and D. Lee Towards clustering of incomplete microarray data without the use of imputation Bioinformatics, January 1, 2007; 23(1): 107 - 113. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
X. Leng and H.-G. Muller Classification using functional data analysis for temporal gene expression data Bioinformatics, January 1, 2006; 22(1): 68 - 76. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. Remondini, B. O'Connell, N. Intrator, J. M. Sedivy, N. Neretti, G. C. Castellani, and L. N. Cooper Targeting c-Myc-activated genes with a correlation method: Detection of global changes in large gene expression network dynamics PNAS, May 10, 2005; 102(19): 6902 - 6906. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D.-W. Kim, K. H. Lee, and D. Lee Detecting clusters of different geometrical shapes in microarray gene expression data Bioinformatics, May 1, 2005; 21(9): 1927 - 1934. [Abstract] [Full Text] [PDF]
|
|