Bioinformatics Vol. 19 no. 16 2003
pages 2088-2096
© 2003 Oxford University Press
A Bayesian missing value estimation method for gene expression profile data
1 Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma 630-0192, Japan, 2 ATR Human Information Science Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan, 3 Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, Japan, 4 DNA Chip Research Institute, 134 Kobecho, Hodogayaku, Yokohama, Japan and 5 CREST, Japan Science and Technology Corporation
Received on March 10, 2003
; revised on May 6, 2003
; accepted on May 9, 2003
Motivation: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes inference is not new in principle, actual BPCA implementation that makes it possible to estimate arbitrary missing variables is new in terms of statistical methodology.
Results: When applied to DNA microarray data from various experimental conditions, the BPCA method exhibited markedly better estimation ability than other recently proposed methods, such as singular value decomposition and K-nearest neighbors. While the estimation performance of existing methods depends on model parameters whose determination is difficult, our BPCA method is free from this difficulty. Accordingly, the BPCA method provides accurate and convenient estimation for missing values.
Availability: The software is available at http://hawaii.aist-nara.ac.jp/~shige-o/tools/
Contact: ishii{at}is.aist-nara.ac.jp
* To whom correspondence should be addressed.
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