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Mathematical Problems in Engineering
Volume 2013, Article ID 162938, 5 pages
Research Article

Missing Value Estimation for Microarray Data by Bayesian Principal Component Analysis and Iterative Local Least Squares

1College of Mechanical and Electronic Engineering, Northwest A&F University, No. 22 Xinong Road, Yangling, Xi'an, Shaanxi 712100, China
2School of Electronics and Information Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an, Shaanxi 710049, China
3Department of Engineering, Faculty of Technology and Science, University of Agder, Service Box 509, 4898 Grimstad, Norway

Received 1 March 2013; Revised 11 March 2013; Accepted 13 March 2013

Academic Editor: Rongni Yang

Copyright © 2013 Fuxi Shi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Missing values are prevalent in microarray data, they course negative influence on downstream microarray analyses, and thus they should be estimated from known values. We propose a BPCA-iLLS method, which is an integration of two commonly used missing value estimation methods—Bayesian principal component analysis (BPCA) and local least squares (LLS). The inferior row-average procedure in LLS is replaced with BPCA, and the least squares method is put into an iterative framework. Comparative result shows that the proposed method has obtained the highest estimation accuracy across all missing rates on different types of testing datasets.