Bioinformatics Advance Access published online on August 23, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti638
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1 Department of Statistics, Rutgers, the State University of New Jersey, New Brunswick, NJ 08903, USA
* To whom correspondence should be addressed.
Motivation: Significance analysis of differential expression in DNA microarray data is an important task. Much of current research is focused on developing improved tests and software tools. The task is difficult due not only to the high dimensionality of the data (number of genes), but also the often non-negligible presence of missing values. There is thus a great need to reliably impute these missing values prior to the statistical analyses. Many imputation methods have been developed for DNA microarray data, but their impact on statistical analyses has not been well studied. In this work we examine how missing values and their imputation affect significance analysis of differential expression. Results: We develop a new imputation method (LinCmb) that is superior to the widely used methods in terms of normalized root mean squared error. Its estimates are the convex combinations of the estimates of existing methods. We find that LinCmb adapts to the structure of the data: If data are heterogeneous or if there are few missing values, LinCmb puts more weight on local imputation methods; if data are homogeneous or if there are many missing values, LinCmb puts more weight on global imputation methods. Thus LinCmb is a useful tool to understand the merits of different imputation methods. We also demonstrate that missing values affect significance analysis. Two data sets, different amounts of missing values, different imputation methods, and the standard t-test, the regularized t-test (Baldi and Long, 2001), and ANOVA are employed in the simulations. We conclude that good imputation alleviates the impact of missing values and should be an integral part of microarray data analysis. The most competitive methods are LinCmb, GMC (Ouyang et al., 2004), and BPCA (Oba et al., 2003). Popular imputation schemes such as SVD, row mean, and KNN all exhibit high variance and poor performance. The regularized t-test is less affected by missing values than the standard t-test.
Received September 24, 2004
Revised August 2, 2005
Accepted August 17, 2005
Article
DNA microarray data imputation and significance analysis of differential expression
2 Stoecker Road, Holmdel, NJ 07733, USA
3 Department of Pharmacology, Robert Wood Johnson Medical School, and Informatics Institute, University of Medicine and Dentistry of New Jersey, Piscataway, NJ 08854, USA
Rebecka Jörnsten, E-mail: rebecka{at}stat.rutgers.edu
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