Stationary wavelet packet transform and dependent laplacian bivariate shrinkage estimator for array-CGH data smoothing

Journal of Computational Biology

Volumn 17, Issue 2, 2010, Pages 139-152

  Nguyen, Nha ^a   Huang, Heng ^a   Oraintara, Soontorn ^a   Vo, An ^b  

^a The University of Texas at Arlington   (United States)

^b FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH   (United States)

Author keywords

Array comparative genomic hybridization; DNA copy number; Smoothing; Stationary wavelet packet transform

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL CHROMOSOME; BIOLOGICAL MODEL; BIOLOGY; COMPARATIVE GENOMIC HYBRIDIZATION; COPY NUMBER VARIATION; DNA MICROARRAY; GENETICS; HUMAN; HUMAN GENOME; METHODOLOGY; NEOPLASM; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; CHROMOSOMES, ARTIFICIAL; COMPARATIVE GENOMIC HYBRIDIZATION; COMPUTATIONAL BIOLOGY; DNA COPY NUMBER VARIATIONS; GENOME, HUMAN; HUMANS; MODELS, BIOLOGICAL; NEOPLASMS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; ROC CURVE;

EID: 77249162726     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2009.0013     Document Type: Article

References (34)

1. Beheshti, B., Braude, I., Marrano, P., et al. 2003. Chromosomal localization of DNA amplifications in neuroblastoma tumors using cDNA microarray comparative genomic hybridization. Neoplasia 5, 53-62.
2. Bilke, S., Chen, Q.R., Whiteford, C.C., et al. 2005. Detection of low level genomic alterations by comparative genomic hybridization based on cDNA microarrays. Bioinformatics 21, 1138-1145.
3. Brennan, C., Zhang, Y., Leo, C., et al. 2004. High-resolution global profiling of genomic alterations with long oligonucleotide microarray. Cancer Res. 64, 4744-4748.
4. Bruce, A., and Gao, H. 1996. Understanding waveshrink: variance and bias estimation. Biometrika 83, 727-745.
5. Chang, S., Yu, B., and Vetterli, M. 2000. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9, 1532-1546.
6. Coifman, R., and Donoho, D. 1995. Translation-invariant de-noising. Lect. Notes Statist. 103, 125-150.
7. Coifman, R., and Wickerhauser, M. 1992. Entropy-based algorithms for best basis selection. IEEE Trans. Inform. Theory 38, 713-718.
8. Donoho, D. 1995. De-noising by soft-thresholding. IEEE Trans. Inform. Theory 41, 613-627.
9. Donoho, D., and Johnstone, I. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425-455.
10. Eilers, P., and De Menezes, R. 2005. Quantile smoothing of array CGH data. Bioinformatics 21, 1146-1153.
11. Fridkyand, J., et al. 2004. Hidden Markov models approach to the analysis of arrray CGH data. J. Multivariate Anal. 90, 132-153.
12. Computational Genomics. 2005. Supplementary material for Lai, w. [2005]. Available at http:==compbio.med.harvard .edu=Supplements=Bioinformatics05b.html. Accessed November 1, 2009.
13. Huang, J., et al. 2007. Robust smooth segmentation approach for array CGH data analysis. Bioinformatics 23, 2463-2469.
14. Hupe, P., Stransky, N., Thiery, J.P., et al. 2004. Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Biostatistics 20, 3413-3422.
15. Johnstone, I., and Silverman, B. 1997. Wavelet threshold estimators for data with correlated noise. J. R. Statist. Soc. 59, 319-351.
16. Jong, K., Marchiori, E., Meijer, G., et al. 2004. Breakpoint identification and smoothing of array comparative genomic hybridization data. Bioinformatics 20, 3636-3637.
17. Lai, W., Johnson, M., Kucherlapati, R., et al. 2005. Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics 21, 3763-3770.
18. Hsu, L., et al. 2005. Denoising array-based comparative genomic hybridization data using wavelets. Biostatistics 6, 211-226.
19. Li, Y., and Zhu, J. 2007. Analysis of array CGH data for cancer studies using fused quantile regression. Bioinformatics 23, 2470-2476.
20. Lingjaerde, O., et al. 2005. CGH-exploxer: a program for analysis of array-CGH data. Bioinformatics 21, 821-822.
21. Myers, C., Dunham, M., Kung, S., et al. 2004. Accurate detection of aneuploidies in array CGH and gene expression microarray data. Bioinformatics 20, 3533-3543.
22. NCBI (National Center for Biotechnology Information). 2008. Gene expression omnibus-GEO. Available at: www.ncbi.nlm.nih.gov=projects=geo=query= acc.cgi?acc=GSE9220. Accessed November 1, 2009.
23. Nguyen, N., Huang, H., Oraintara, S., et al. 2007. A new smoothing model for analyzing array CGH data. IEEE BIBE.
24. Olshen, A., Venkatraman, E., Lucito, R., et al. 2004. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557-572.
25. Picard, F., et al. 2005. A statistical approach for array CGH data analysis. BMC Bioinform. 27.
26. Pinkel, D., Segraves, R., Sudar, D., et al. 1998. High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet. 20, 207-211.
27. Pollack, J., Perou, C., Alizadeh, A., et al. 1999. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nat. Genet. 23, 41-46.
28. Sendur, L., and Selesnick, I. 2002. Bivariate shinkage function for wavelet-based denoising exploiting interscale dependency. IEEE Trans. Signal Process. 50, 11.
29. Snijders, A.M., Nowak, N., Segraves, R., et al. 2001. Assembly of microarrays for genome-wide measurement of DNA copy number. Nat. Genet. 29, 263-264.
30. Strang, G., and Nguyen, T. 1996. Wavelets and Filter Banks. Wellesley-Cambridge Press, Cambridge, MA.
31. Stanford University. 2001. Assembly of microarrays for genome-wide measurement of DNA copy number. Available at: www.nature.com=ng=journal=v29=n3= suppinfo=ng754-S1.html. Accessed November 1, 2009.
32. Wang, P., et al. 2005. A method for callling gains and losses in arrray CGH data. Bioinformatics 6, 45-58.
33. Wang, Y., and Wang, S. 2007. A novel stationary wavelet denoising algorithm for array-based DNA copy number data. Int. J. Bioinform. Res. Appl. 3, 206-222.
34. Willenbrock, H., and Fridlyand, J. 2005. A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics 21, 4084-4091.