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A Penalized Spline Based Method for Detecting the DNA Copy Number Alteration in an Array-CGH Experiment
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 Title & Authors
A Penalized Spline Based Method for Detecting the DNA Copy Number Alteration in an Array-CGH Experiment
Kim, Byung-Soo; Kim, Sang-Cheol;
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The purpose of statistical analyses of array-CGH experiment data is to divide the whole genome into regions of equal copy number, to quantify the copy number in each region and finally to evaluate its significance of being different from two. Several statistical procedures have been proposed which include the circular binary segmentation, and a Gaussian based local regression for detecting break points (GLAD) by estimating a piecewise constant function. We propose in this note a penalized spline regression and its simultaneous confidence band(SCB) approach to evaluate the statistical significance of regions of genetic gain/loss. The region of which the simultaneous confidence band stays above 0 or below 0 can be considered as a region of genetic gain or loss. We compare the performance of the SCB procedure with GLAD and hidden Markov model approaches through a simulation study in which the data were generated from AR(1) and AR(2) models to reflect spatial dependence of the array-CGH data in addition to the independence model. We found that the SCB method is more sensitive in detecting the low level copy number alterations.
DNA copy number alteration;gastric cancer;penalized spline;simultaneous confidence band;
 Cited by
Barry, D. and Hartigan, J. A. (1993). A Bayesian analysis for change point problems, Journal of the American Statistical Association, 88, 309-319 crossref(new window)

Broet, P. and Richardson, S. (2006). Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model, Bioinformatics, 22, 911-918 crossref(new window)

Chari, R., Lockwood, W. W. and Lam, W. L. (2006). Computational methods for the analysis of array comparative genomic hybridization, Cancer Informatics, 2, 48-58

Eilers, P. H. C and de Menezes, R X. (2005). Quantile smoothing of array CGH data, Bioinformatics, 21, 1146-1153 crossref(new window)

Fan, J. and Niu, Y. (2007). Selection and validation of normalization methods fore-DNA microarrays using within-array replications, Bioinformatics, 23, 2391-2398 crossref(new window)

Fridlyand, J., Snijders, A. M., Pinkel, D., Albertson, D. G. and Jain, A. N. (2004). Hidden Markov models approach to the annlysis of array CGH data, Journal of Multivariate Analysis, 90, 132-153 crossref(new window)

Henderson, C R. (1975). Best linear unbiased estimation and prediction under a selection model, Biometrics, 31, 423-447 crossref(new window)

Hsu, L., Self, S. G., Grove, D., Randolf, T., Wang, K., Delrow, J. J., Loo, L. and Porter, P. (2005). Denoising array-based comparative genomic hybridization data using wavelets, Biostatistics, 6, 211-226 crossref(new window)

Huang, T., Wu, B., Lizardi, P. and Zhao, H. (2005). Detection of DNA copy number alterations using penalized least squares regression, Bioinformatics, 21, 3811-3817 crossref(new window)

Hupe, P., Stransky, N., Thiery, J. P., Radvanyi, F. and Barillot, E. (2004). Analysis of array CGH data: From signal ratio to gain and loss of DNA regions, Bioinformatics, 20, 3413-3422 crossref(new window)

Jong, K., Marchiori, E., Meijer, G., Vaart, A. V. D. and Ylstra, B. (2004). Breakpoint identification and smoothing of array comparative genomic hybridization data, Bioinformatics, 20, 3636-3637 crossref(new window)

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

Lai, W. R., Johnson, M. D., Kucherlapati, R. and Park, P. J. (2005). Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data, Bioinformatics, 21, 3763-3770 crossref(new window)

Li, Y. and Zhu, J. (2007). Analysis of array CGH data for cancer studies using fused quantile regression, Bioinformatics, 23, 2470-2476 crossref(new window)

Mestre-Escorihuela, C, Rubio-Moscardo, F., Richter, J. A., Seibert, R, Clement, J., Fresquet, V., Beltran, E., Agirre, X., Marugan, I., Marin, M., Rosenwald, A., Sugimoto, K. J., Wheat, L. M., Karran, E. L., Garcia, J. F., Sanchez. L., Prosper, F., Staudt, L. M., Pinkel, D., Dyer, M. J. and Martinez-Climent, J. A. (2007). Homozygous deletions localize novel tumor suppressor gene in B-cell lymphoma, Blood, 109, 271-280 crossref(new window)

Myers, C L., Dunham, M. J., Kung, S. Y. and Troyanskaya, O. G. (2004). Accurate detection of aneuploidies in array CGH and gene expression microaray data, Bioinformatics, 20, 3533-3543 crossref(new window)

Olshen, A. B., Venkatraman, E. S., Lucito, Rand Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data, Biostatistics; 5, 557-572 crossref(new window)

Picard, F., Robin,S., lebarbier, E. and Daudin, J-.J. (2007). A segmentation/clustering model for the analysis of array CGH data, Biometrics, 63, 758-766 crossref(new window)

Pinkel, D. and Albertson, D. G. (2005). Array comparative genomic hybridization and its applications in cancer, Nature Genetics, 37, S11-S17 crossref(new window)

Pollack, J. R, Sorlie, T., Perou, C M., Rees, C A., Jeffrey, S. S., Lonning, P. E., Tibshirani, R, Botstein, D., Borresen-Dale, A. L. and Brown, P. O. (2002). Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors, Proceedings of the National Academy of Sciences, 99, 12963-12968 crossref(new window)

Rabiner, L. R (1989). A tutorial on hidden Markov models and selected applications in speech recognition, In Proceedings of the IEEE, 77, 257-286 crossref(new window)

Rigaill, G., Hupe, P., LaRosa, P., Meyniel, J-.P., Decraene, C, Almeida, A. and Barillot, E. (2008). ITALICS: An algorithm for normalization and DNA copy number calling for Affymetrix SNP arrays, Bioinformatics, 24, 768-774 crossref(new window)

Rouveirol, C, Stransky, N., Hupe, P., Rosa, P. L., Viara, E., Barillot, E. and Radvanyi, F. (2006). Computation of recurrent minimal genomic alterations from array-CGH data, Bioinformatics, 22, 849-856 crossref(new window)

Ruppert, D., Wand, M. P. and Carroll, R. J. (2003). Semiparametric Regression, Cambridge University Press, New York

Scheel, I., Aldrin, M., Glad, I. K., Sorum, R., Lying, H, and Frigessi, A. (2005). The inference of missing value imputation on detection of differentially expressed genes from microarray data, Bioinformatics, 21, 4272-4279 crossref(new window)

Shah, S. P., Lam, W. L., Ng, R. T. and Murphy, K. P. (2007). Modeling recurrent DNA copy number alterations in array CGH data, Bioinformatics, 23, i450-i458 crossref(new window)

Stjernqvist, S., Ryden, T., Skold, M. and Staaf, J. (2007). Continuous-index hidden Markov modelling of array CGH copy number data, Bioinformatics. 23, 1006-1014 crossref(new window)

Tibshirani, R. and Wang, P. (2008). Spatial smoothing and hot spot detection for CGH data using the fused lasso, Biostatistics, 9, 18-29 crossref(new window)

Venkatraman, E. S. and Olshen, A. B. (2007). A faster circular binary segmentation algorithm for the analysis of array CGH data, Bioinformatics, 23, 657-663 crossref(new window)

Wen, C.C., Wu, Y-J., Huang, Y-H., Chen, W-C., Liu, S-C., Jiang, S. S., Juang, J. L., Lin, C. Y., Fang, W. T., Hsiung, C. A. and Chang, I. S. (2006). A Bayes regression approach to array-CGH data, Statistical Applications in Genetics and Molecular Biology, 5, Article 3 crossref(new window)

Yang, S. (2007). Gene amplifications at chromosome 7 of the human gastric cancer genome, International Journal of Molecular Medicine, 20, 225-231

Yang,S., Jeung, H. C., Choi, Y. H., Kim, J. E., Jung, J-J., Jeong, H. J., Rha, S. Y., Yang, W. I. and Chung, H. C. (2007). Identification of genes with correlated patterns of variations in DNA copy number and gene expression level in gastric cancer, Genomics, 89, 451-459 crossref(new window)

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

Yistra, B., van der lJssel, P., Carvalho, B., Brakenhoff, R. H. and Meijer, G. A. (2006). BAC to the future! or oligonucleotides: A perspective for micro array comparative genomic hybridization(array CGH), Nucleic Acid Research, 34, 445-450 crossref(new window)