Missing value estimation for microarray data by Bayesian principal component analysis and iterative local least squares
Journal article, Peer reviewed
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Original versionShi, F.X., Zhang, D., Chen, J., & Karimi, H.R. (2013). Missing value estimation for microarray data by Bayesian principal component analysis and iterative local least squares. Mathematical Problems in Engineering. doi: 10.1155/2013/162938 10.1155/2013/162938
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.
Published version of an article from the journal: Mathematical Problems in Engineering. Also available from Hindawi: http://dx.doi.org/10.1155/2013/162938