Normalization of DNA-Microarray Data by Nonlinear Correlation Maximization

Journal of Computational Biology

Volumn 10, Issue 5, 2003, Pages 751-762

  Faller, D ^a   Voss, H U ^a   Timmer, J ^a   Hobohm, U ^b,c  

^a UNIVERSITY OF FREIBURG   (Germany)

^b F HOFFMANN LA ROCHE LTD   (Switzerland)

^c UNIVERSITY OF APPLIED SCIENCES   (Germany)

Author keywords

DNA microarray; Nonlinear normalization

Indexed keywords

DNA MICROARRAY; MATHEMATICAL GENETICS; NOISE REDUCTION; POLYMERASE CHAIN REACTION; PRIORITY JOURNAL; REVIEW;

ALGORITHMS; DNA; FALSE NEGATIVE REACTIONS; FALSE POSITIVE REACTIONS; NUCLEIC ACID HYBRIDIZATION; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 0242440112     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/106652703322539079     Document Type: Review

References (19)

1. Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., and Levine, A. 1999. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci USA. 96(12), 6745-6750.
2. Amaratunga, D., and Cabrera, J. 2000. Outlier resistance, standardization, and modeling issues for DNA microarray data. Preprint.
3. Amaratunga, D., and Cabrera, J. 2001. Analysis of data from viral DNA microchips. J. Am. Statist. Assoc. 96(456), 1161-1170.
4. Breiman, L., and Friedman, J.H. 1985. Estimating optimal transformations for multiple regression and correlation. J. Am. Statist. Assoc. 80, 580.
5. Claverie, J.M. 1999. Computational methods for the identification of differential and coordinated gene expression. Hum. Mol. Genet. 8, 1821-1832.
6. Dudoit, S., Yang, Y.H., Callow, M.J., and Speed, T.P. 2000. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Technical Report No. 578, Department of Biochemistry, Stanford University.
7. Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, D., and Lander, E. 1999. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286, 929-934.
8. Härdle, W. 1990. Applied Nonparametric Regression, Cambridge University Press, New York.
9. Ideker, T., Thorsson, V., Ranish, J., Christmas, R., Buhler, J., Bumgarner, R., Aebersold, R., and Hood., L. 2001. Integrated genomic and proteomic analysis of a systematically perturbed metabolic network. Science 292, 929-934.
10. Kerr, M.K., and Churchill, G.A. 2001. Experimental design for gene expression microarrays. Biostatistics 2, 183-201.
11. Rényi, A. 1959. On measures of dependence. Acta. Math. Acad. Sci. Hungary 10, 441-451.
12. Schadt, E.E., Li, C., Ellis, B., and Wong, W.H. 1999. Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. Technical Report No. 303, Department of Statistics, UCLA.
13. Schadt, E.E., Li, C., Su, C., and Wong, W.H. 2001. Analyzing high-density oligonucleotide gene expression array data. J. Cell. Biochem. 80, 192-202.
14. Schimek, M., ed. 2000. Smoothing and Regression. Approaches, Computation and Application, Wiley, New York.
15. Schuchhardt, J., Beule, D., Malik, A., Wolski, E., Eickhoff, H., Lehrach, H., and Herzel, H. 2000. Normalization strategies for cDNA microarrays. Nucl. Acids Res. 28(10), e47.
16. Tusher, V.G., Tibshirani, R., and Chu, G. 2001. Significance analysis of microarrays applied to the ionizing radiation responses. Proc. Natl. Acad. Sci. 98, 5116-5121.
17. Voss, H.U. 2001. Analyzing nonlinear dynamical systems with nonparametric regression, in A. Mees, ed., Nonlinear Dynamics and Statistics, 413-434, Birkhäuser, Boston.
18. Yang, Y.H., Dudoit, S., Luu, P., and Speed, T.P. 2000. Normalization of cDNA micmarray data. Technical Report, Department of Statistics, UC Berkeley.
19. Zien, A., Aigner, T., Zimmer, R., and Lengauer, T. 2001. Centralization: A new method for the normalization of gene expression data. Bioinformatics 17, 323-331.