Title
A Bayesian missing value estimation method for gene expression profile data.
Abstract
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.
Year
DOI
Venue
2003
10.1093/bioinformatics/btg287
BIOINFORMATICS
Keywords
Field
DocType
singular value decomposition,principal component analysis,gene expression,biology,multivariate analysis,treatment,latent variable,missing values,gene expression profiling,probabilistic model
Data mining,Inference,Computer science,Latent variable,Statistical model,Imputation (statistics),Missing data,Principal component analysis,Bayesian probability,Bayes' theorem
Journal
Volume
Issue
ISSN
19
16.0
1367-4803
Citations 
PageRank 
References 
184
13.35
4
Authors
6
Search Limit
100184
Name
Order
Citations
PageRank
Shigeyuki Oba129027.68
Masa-aki Sato222418.60
Ichiro Takemasa318813.99
Morito Monden425326.12
Ken-ichi Matsubara519315.33
Shin Ishii653243.99