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BMCBI
2007

Robust imputation method for missing values in microarray data

10 years 2 months ago
Robust imputation method for missing values in microarray data
Background: When analyzing microarray gene expression data, missing values are often encountered. Most multivariate statistical methods proposed for microarray data analysis cannot be applied when the data have missing values. Numerous imputation algorithms have been proposed to estimate the missing values. In this study, we develop a robust least squares estimation with principal components (RLSP) method by extending the local least square imputation (LLSimpute) method. The basic idea of our method is to employ quantile regression to estimate the missing values, using the estimated principal components of a selected set of similar genes. Results: Using the normalized root mean squares error, the performance of the proposed method was evaluated and compared with other previously proposed imputation methods. The proposed RLSP method clearly outperformed the weighted k-nearest neighbors imputation (kNNimpute) method and LLSimpute method, and showed competitive results with Bayesian prin...
Dankyu Yoon, Eun-Kyung Lee, Taesung Park
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Dankyu Yoon, Eun-Kyung Lee, Taesung Park
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