arraylmpute: Software for Exploratory Analysis and Imputation of Missing Values for Microarray Data

  • Lee, Eun-Kyung (Department of Clinical Pharmacology and Therapeutics, University of Ulsan) ;
  • Yoon, Dan-Kyu (Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Park, Tae-Sung (Department of Statistics, Seoul National University)
  • Published : 2007.09.30

Abstract

arraylmpute is a software for exploratory analysis of missing data and imputation of missing values in microarray data. It also provides a comparative analysis of the imputed values obtained from various imputation methods. Thus, it allows the users to choose an appropriate imputation method for microarray data. It is built on R and provides a user-friendly graphical interface. Therefore, the users can easily use arraylmpute to explore, estimate missing data, and compare imputation methods for further analysis.

Keywords

References

  1. Feten, G., Almoy, T., and Aastveit, A.H. (2005). Prediction of Missing Values in Microarray and Use of Mixed Models to Evaluate the Predictors Stat Appl Genet Mol Biol. 4, Article10
  2. Kim, H., Golub, G.H., and Park, H. (2005). Missing Value Estimation for DNA microarray gene expression data: local least squares imputation. Bioinformatics 21, 187-198 https://doi.org/10.1093/bioinformatics/bth499
  3. Oba, S., Sato, M., Takemasa, I., Monden, M., Matsubara, K, and Ishii, S. (2003). A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19, 2088-2096 https://doi.org/10.1093/bioinformatics/btg287
  4. Scheel, I., Aldrin, M., Glad, I., Sorum, R, Lyun, H., and Frigessi, A. . (2005). The influence of missing value imputation on detection of differentially expressed genes from microarray data. Bioinformatics 21 , 4272-4279. https://doi.org/10.1093/bioinformatics/bti708
  5. Troyanskaya, O., Cantor, M., Sherlock, G. Brown, P., Hastie, T.,Tibshirani, R., Botstein, D., and Altman, R. B. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520-525 https://doi.org/10.1093/bioinformatics/17.6.520
  6. Yoon, D., Lee, E.K., and Park, T. (2007). Robust imputation method for missing values in microarray data. BMC Bioinformatics 8, S6 https://doi.org/10.1186/1471-2105-8-6
  7. Wang, D., Lv, Y., Guo, Z., Li, X., Li, Y., Zhu, J., Yang, D., Xu, J., Wang C., Rao, S. and Yang, B. (2006). Effects of replacing the unreliable cDNA microarray measurements on the disease classification based on gene expression profiles and functional modules. Bioinformatics 22, 2883-2889 https://doi.org/10.1093/bioinformatics/btl339