Bioinformatics Vol. 17 no. 6 2001
Pages 520-525
© 2001 Oxford University Press
Missing value estimation methods for DNA microarrays
1 Stanford Medical Informatics
2 Department of Genetics, Stanford
University School of Medicine, Stanford, CA, USA
3 Department of Biochemistry, Stanford
University School of Medicine, and Howard Hughes Medical Institute,
Stanford, CA, USA
4 Departments of Statistics and Health
Research and Policy, Stanford University, Stanford, CA, USA
Received on November 13, 2000
; revised on February 22, 2001
; accepted on February 26, 2001
Motivation: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data.
Results: We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1–20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.
Availability: The software is available at http://smi-web.stanford.edu/projects/helix/pubs/impute/
Contact: russ.altman{at}stanford.edu
* To whom correspondence should be addressed.
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M. Scholz, F. Kaplan, C. L. Guy, J. Kopka, and J. Selbig Non-linear PCA: a missing data approach Bioinformatics, October 15, 2005; 21(20): 3887 - 3895. [Abstract] [Full Text] [PDF]
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A. Frigessi, M. A. van de Wiel, M. Holden, D. H. Svendsrud, I. K. Glad, and H. Lyng Genome-wide estimation of transcript concentrations from spotted cDNA microarray data Nucleic Acids Res., October 4, 2005; 33(17): e143 - e143. [Abstract] [Full Text] [PDF]
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J. Y. King, R. Ferrara, R. Tabibiazar, J. M. Spin, M. M. Chen, A. Kuchinsky, A. Vailaya, R. Kincaid, A. Tsalenko, D. X.-F. Deng, et al. Pathway analysis of coronary atherosclerosis Physiol Genomics, September 21, 2005; 23(1): 103 - 118. [Abstract] [Full Text] [PDF]
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S. Balaji, M. M. Babu, L. M. Iyer, and L. Aravind Discovery of the principal specific transcription factors of Apicomplexa and their implication for the evolution of the AP2-integrase DNA binding domains Nucleic Acids Res., July 21, 2005; 33(13): 3994 - 4006. [Abstract] [Full Text] [PDF]
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R. Tabibiazar, R. A. Wagner, E. A. Ashley, J. Y. King, R. Ferrara, J. M. Spin, D. A. Sanan, B. Narasimhan, R. Tibshirani, P. S. Tsao, et al. Signature patterns of gene expression in mouse atherosclerosis and their correlation to human coronary disease Physiol Genomics, July 14, 2005; 22(2): 213 - 226. [Abstract] [Full Text] [PDF]
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L. J. Lu, Y. Xia, A. Paccanaro, H. Yu, and M. Gerstein Assessing the limits of genomic data integration for predicting protein networks Genome Res., July 1, 2005; 15(7): 945 - 953. [Abstract] [Full Text] [PDF]
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C. Romualdi, N. Vitulo, M. D. Favero, and G. Lanfranchi MIDAW: a web tool for statistical analysis of microarray data Nucleic Acids Res., July 1, 2005; 33(suppl_2): W644 - W649. [Abstract] [Full Text] [PDF]
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J. Gui and H. Li Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data Bioinformatics, July 1, 2005; 21(13): 3001 - 3008. [Abstract] [Full Text] [PDF]
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G. Rondeau, M. McClelland, T. Nguyen, R. Risques, Y. Wang, M. Judex, A. H. Cho, and J. Welsh Enhanced microarray performance using low complexity representations of the transcriptome Nucleic Acids Res., June 24, 2005; 33(11): e100 - e100. [Abstract] [Full Text] [PDF]
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A. M. Levin, D. Ghosh, K. R. Cho, and S. L. R. Kardia A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors Bioinformatics, June 15, 2005; 21(12): 2867 - 2874. [Abstract] [Full Text] [PDF]
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