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Bioinformatics Advance Access published online on October 31, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl555
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 11, 2005
Revised October 8, 2006
Accepted October 24, 2006

Article

Towards clustering of incomplete microarray data without the use of imputation

Dae-Won Kim 1, Ki-Young Lee 2, Kwang H. Lee 2, and Doheon Lee 2 *

1 School of Computer Science and Engineering, Chung-Ang University, Seoul City, Republic of Korea
2 Department of BioSystems, KAIST, Daejeon City, Republic of Korea


  Abstract

Motivation: Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arises in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approach is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent process of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process.

Results: We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g., k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data.

Availability: The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.

Associate Editor: Satoru Miyano
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