Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes

BMC Bioinformatics
Guy N BrockGeorge C Tseng

Abstract

Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures x time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expr...Continue Reading

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Citations

Mar 4, 2009·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Juliette Blanchet, Matthieu Vignes
Sep 9, 2011·Briefings in Bioinformatics·Jui-Hung HungCharles DeLisi
Mar 1, 2012·Nucleic Acids Research·Sébastien Tempel, Fariza Tahi
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May 30, 2009·BMC Bioinformatics·Sanghamitra Bandyopadhyay, Malay Bhattacharyya
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Jan 9, 2010·BMC Bioinformatics·In-Hwan PaikWoon-Kee Paek

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Methods Mentioned

BETA
chip
PCA

Software Mentioned

STS
R
POCS
Matlab
LSA

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