Prediction of missing values in microarray and use of mixed models to evaluate the predictors. Feten, G., Almoy, T., & Aastveit, A. Statistical Applications In Genetics and Molecular Biology, BERKELEY ELECTRONIC PRESS, 2809 TELEGRAPH AVENUE, STE 202, BERKELEY, CA 94705 USA, 2005.
doi  abstract   bibtex   
Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six imputation methods. A comparative study of the methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. The study showed that different methods' capability to predict is dependent on the data, hence the ideal choice of method and number of components are different for each data set. For data with correlation structure methods based on K-nearest neighbours seemed to be best, while for data without correlation structure using the average of the gene was to be preferred.
@article{Feten:2005aa,
	Abstract = {Gene expression microarray experiments generate data sets with multiple missing expression values. In some cases, analysis of gene expression requires a complete matrix as input. Either genes with missing values can be removed, or the missing values can be replaced using prediction. We propose six imputation methods. A comparative study of the methods was performed on data from mice and data from the bacterium Enterococcus faecalis, and a linear mixed model was used to test for differences between the methods. The study showed that different methods' capability to predict is dependent on the data, hence the ideal choice of method and number of components are different for each data set. For data with correlation structure methods based on K-nearest neighbours seemed to be best, while for data without correlation structure using the average of the gene was to be preferred.},
	Address = {2809 TELEGRAPH AVENUE, STE 202, BERKELEY, CA 94705 USA},
	Author = {Feten, G. and Almoy, T. and Aastveit, A.H.},
	Date = {2005},
	Date-Added = {2008-08-05 15:55:04 -0400},
	Date-Modified = {2014-09-26 15:13:48 +0000},
	Doi = {ARTN 10},
	Journal = {Statistical Applications In Genetics and Molecular Biology},
	Publisher = {BERKELEY ELECTRONIC PRESS},
	Timescited = {6},
	Title = {Prediction of missing values in microarray and use of mixed models to evaluate the predictors},
	Volume = {4},
	Year = {2005},
	Bdsk-Url-1 = {http://dx.doi.org/10}}

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