Browse > Article
http://dx.doi.org/10.5351/KJAS.2008.21.2.275

Missing Values Estimation for Time Course Gene Expression Data Using the Sequential Partial Least Squares Regression Fitting  

Kim, Kyung-Sook (Dept. of Statistics, Chonnam National University)
Oh, Mi-Ra (Dept. of Information and Communications, Gwangju Institute of Science Technology)
Baek, Jang-Sun (Dept. of Statistics, Chonnam National University)
Son, Young-Sook (Dept. of Statistics, Chonnam National University)
Publication Information
The Korean Journal of Applied Statistics / v.21, no.2, 2008 , pp. 275-290 More about this Journal
Abstract
The size of microarray gene expression data is very big and its observation process is also very complex. Thus missing values are frequently occurred. In this paper we propose the sequential partial least squares(SPLS) regression fitting method to estimate missing values for time course gene expression data that has correlations among observations over time points. The SPLS method is to combine the sequential technique with the partial least squares(PLS) regression fitting method. The usefulness of method proposed is evaluated through some simulation study for three yeast time course data.
Keywords
Microarray; time course gene expression data; missing value estimation; partial least squares regression fitting; sequential partial least squares regression fitting;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. and Futcher, B. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization, Molecular Biology of the Cell, 9, 3273-3297   DOI
2 Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Bostein, D. and Altman, R. B. (2001). Missing value estimation methods for DNA microarrays, Bioinformatics, 17, 520-525   DOI   ScienceOn
3 Jorgensen, B. and Goegebeur, Y. (2006). Module 8: Partial least squares regressions II, STO2: Multivariate Data Analysis and Chemometrics, http://statmaster.sdu.dk/cour-ses/ST02
4 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   DOI   ScienceOn
5 Kim, K. Y., Kim, B. J. and Yi, G. S. (2004). Reuse of imputed data in microarray analysis increases imputation efficiency, BMC Bioinformatics, 5, 160   DOI   ScienceOn
6 Nguyen, D. V. and Rocke, D. M. (2002). Tumor classification by partial least squares using microarray gene expression data, Bioinformatics, 18, 39-50   DOI   ScienceOn
7 Nguyen, D., Wang, N. and Carroll, R. J. (2004). Missing value estimation for cancer microarray gene. expression data, Journal of Data Science, 2, 347-370
8 DeRisi, J. L., Iyer, V. R. and Brown, P. O. (1997). Exploring the metabolic and genetic control of gene expression on a genomic scale, Science, 278, 680-686   DOI   ScienceOn
9 Garthwaite, P. H. (1994). An interpretation of partial least squares, Journal of the American Statistical Association, 89, 122-127   DOI
10 Hastie, T., Alter, O., Sherlock, G., Eisen, M., Tibshirani, R., Bostein, D. and Brown, P. (1999). Imputation of missing values in DNA microarrays, Technical Report Stanford University Statistics Department
11 Hoskuldsson, A. (1988). PLS regression methods, Journal of Chemometrics, 2, 211-228   DOI
12 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, Bioinfor-matics, 19, 2088-2096   DOI   ScienceOn
13 Abdi, H. (2003). Partial least squares regression (PLS-regression), In M. Lewis-Beck, A. Bryman, T. Futing (Eds): Encyclopedia for research methods for the social sciences, Thousand Oaks (CA): Sage, 792-795
14 de Brevern, A. G., Hazout, S. and Malpertuy, A. (2004). Influence of microarrays experi- ments missing values on the stability of gene groups by hierarchical clustering, BMC Bioinformatics, 5, 114
15 B/o, T. H., Dysvik, B. and Jonassen, I. (2004). LSimpute: Accurate estimation of missing values in microarray data with least squares methods, Nucleic Acids Research, 32, e34   DOI   ScienceOn