JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Concordance Study of the Preprocessing Orders in Microarray Data
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Concordance Study of the Preprocessing Orders in Microarray Data
Kim, Sang-Cheol; Lee, Jae-Hwi; Kim, Byung-Soo;
  PDF(new window)
 Abstract
Researchers of microarray experiment transpose processed images of raw data to possible data of statistical analysis: it is preprocessing. Preprocessing of microarray has image filtering, imputation and normalization. There have been studied about several different methods of normalization and imputation, but there was not further study on the order of the procedures. We have no further study about which things put first on our procedure between normalization and imputation. This study is about the identification of differentially expressed genes(DEG) on the order of the preprocessing steps using two-dye cDNA microarray in colon cancer and gastric cancer. That is, we check for compare which combination of imputation and normalization steps can detect the DEG. We used imputation methods(K-nearly neighbor, Baysian principle comparison analysis) and normalization methods(global, within-print tip group, variance stabilization). Therefore, preprocessing steps have 12 methods. We identified concordance measure of DEG using the datasets to which the 12 different preprocessing orders were applied. When we applied preprocessing using variance stabilization of normalization method, there was a little variance in a sensitive way for detecting DEG.
 Keywords
Concordance measure;imputation;microarray;normalization;preprocessing;t3 statistic;
 Language
Korean
 Cited by
1.
당귀(當歸)가 다낭성난소증후군이 유발된 흰쥐 난소조직의 유전자 발현에 미치는 영향,류기준;조성희;

대한한방부인과학회지, 2011. vol.24. 3, pp.28-47
2.
마황 에틸아세테이트 분획물이 고지방 식이로 유발된 생쥐의 지질대사에 미치는 영향,하태훈;권태우;김영균;

대한예방한의학회지, 2014. vol.18. 2, pp.101-113
 References
1.
Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing, Journal oj the Royal Statistical Society Series B, 57, 289-300

2.
Bolstad, B. M., Irizarry, R. A, Astrand, M. and Speed, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias, Bioinformatics, 19, 185-193 crossref(new window)

3.
Bo, T. H., Dysvik, B. and Jonassen, I. (2004). LSimpute: Accurate estimation of missing value in microarray data with least squares methods, Nucleic Acide Research, 32, e34 crossref(new window)

4.
Ge, Y., Dudoit, S. and Speed, T. P. (2003). Resampling-based multiple testing for microarray data analysis, Test, 12, 1-77 crossref(new window)

5.
Huber, W., von Heydebreck, A., Siiltmann, H., Poustka, A. and Vingron, M. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Bioinformatics, 18, S96-S104 crossref(new window)

6.
Kim, B. S., Benner, A. and Kim, S. C. (2006). Development of a molecular prognostic indicator of gastric cancer using the penalized Cox regression, <한국통계학회 2006년 춘계학술발표회 논문집>, 41

7.
Kim, B. S., Kim, I., Lee, S., Kim, S., Rha, S. Y. and Chung, H. C. (2005). Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer, Bioinformatics, 21, 517-528 crossref(new window)

8.
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 crossref(new window)

9.
Oba, S., Sato, M. A., Takemasa, I., Monden, M., Matsubara, K. I. and Ishii, S. (2003). A Bayesian missing value estimation method for gene expression profile data, Bioinformatics, 19, 2088-2096 crossref(new window)

10.
Ouyang, M., Welsh, W. J. and Georgopoulos, P. (2004). Gaussian mixture clustering and imputation of microarray data, Bioinformatics, 20, 917-923 crossref(new window)

11.
Sehgal, M. S., Gondal, I. and Dooley, L. S. (2005). Collateral missing value imputation: A new robust missing value estimation algorithm for microarray data, Bioinformatics, 21, 2417-2423 crossref(new window)

12.
Smyth, G. K. and Speed, T. (2003). Normalization of cDNA microarray data, Methods, 31, 265-273 crossref(new window)

13.
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 crossref(new window)

14.
Westfall, P. H. and Young, S. S. (1993). Resampling-Based Multiple Testing: Examples and Methods for p-value Adjustment, John Wiley & Sons, New York, 116-117

15.
Wit, E. and McClure, J. (2004). Statistics for Microarrays: Design, Analysis and Inference, Wiley, New York, 71

16.
Workman, C., Jensen, L. J., Jarmer, H., Berka, R., Gautier, L., Nielser, H. B., Saxild, H. H., Nielsen, C., Brunak, S. and Knudsen, S. (2002). A new non-linear normalization method for reducing variability in DNA microarray experiments, Genome Biology, 3, research0048

17.
Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng, V., Ngai, J. and Speed, T. P. (2002). Normalization for cDNA microarray data: A robust composite method addressing single and multiple slide systematic variation, Nucleic Acids Research, 30, e15 crossref(new window)