E-learning in analysis of genomic and proteomic data 2. Data analysis 2.2. Analysis of high-density genomic data 2.2.1. DNA microarrays Analysis of arrayCGH

[1] Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19 (1974) 716–723.

[2] Andersson,R. et al. : A segmental maximum a posteriori approach to genome wide copy number profiling. Bioinformatics 24 (2008) 751–758.

[3] Autio,R., S. Hautaniemi, P. Kauraniemi, O. Yli-Harja, J. Astola, M. Wolf, and A. Kallioniemi.: CGH-plotter: MATLAB toolbox for cgh-data analysis. Bioinformatics 13 (2003) 1714-1715.

[4] Bauer,D.F.: Constructing confidence sets using rank statistics. J. Am. Stat. Assoc. 67 (1972), 687–690.

[5] Björkholm,B. et al.: Comparison of genetic divergence and fitness between two subclones of Helicobacter pylori. Infect. Immun. 69 (2001) 7832–7838.

[6] Broët, P. & Richardson, S.: Detection of gene copy number changes in cgh microarrays using a spatially correlated mixture model. Bioinformatics, 22 (2006), 911–918.

[7] Budinska,E., Gelnarova,E. & Schimek,M.G.: MSMAD: A computationally efficient method for the analysis of noisy array CGH data. Submitted (2008)

[8] Cahill,D.P., Kinzler,K.W., Vogelstein,B., Lengauer,C.: Genetic instability and Darwinian selection in tumours. Trends Cell Biol. 9 (1999) M57-M60.

[9] Crawley,J.J. & Furge,K.A.: Identification of frequent cytogenetic aberrations in hepatocellular carcinoma using gene-expression microarray data. Genome Biology 3 (2002) 8 p

[10] Dehan,E. et al.: Chromosomal aberrations and gene expression profiles in non-small cell lung cancer. Lung Cancer 56 (2007) 175 -184

[11] Diskin,C. et al.: STAC: A method for testing the significance of DNA copy number aberrations across multiple array-CGH experiments. Genome Res 16 (2006) 1149-1158.

[12] Eilers,P.H.C. and de Menezes,R.X.: Quantile smoothing of array CGH data. Bioinformatics 21 (2005) 1146–1153.

[13] Engler,D.A. et al.: A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations. Biostatistics 7 (2006) 399–421.

[14] Fischer,G., James, S.A., Roberts, I.N., Oliver, S.G., Louis, E.J.: Chromosomal evolution in Saccharomyces. Nature 405 (2000) 451-454

[15] Fridlyand,J. et al.: Hidden Markov models approach to the analysis of array CGH data. J. Multivariate Anal. 90 (2004) 132–153

[16] Guha.S. et al.: Bayesian Hidden Markov Modeling of Array CGH Data. Harvard University Biostatistics Working Paper Series, Harvard School of Public Health (2006)

[17] Haslinger,Ch. et al.: Microarray Gene Expression Profiling of B-Cell Chronic Lymphocytic Leukemia Subgroups Defined by Genomic Aberrations and VH Mutation Status. Journal of Clinical Oncology, 22 (2004) 3937-3949

[18] Hsu,L. et al.: Denoising array-based comparative genomic hybridization data using wavelets. Biostatistics 6 (2005) 211–226.

[19] Huang,J. et al.: Robust smooth segmentation approach for array CGH data analysis. Bioinformatics 23 (2007) 2463-2469.

[20] Hupé,P. et al.: Analysis of array CGH data: from signal ratio to gain and loss of

DNA regions. Bioinformatics 20 (2004) 3413–3422

[21] Jong,K. et al.: Chromosomal breakpoint detection in human cancer. In Lecture Notes in ComputerScience, Springer-Verlag, Berlin, Vol. 2611 (2003) 54–65.

[22] Kaufman,L., Rousseeuw,P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)

[23] Lai,W.R. et al.: Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics 21 (2005) 3763–3770

[24] Lai,T.L., Xing, H., Zhang, N.: Stochastic segmentation models for array-based comparative genomic hybridization data analysis. Biostatistics 9 (2008) 290–307

[25] Lavielle,M.: On the use of penalized contrasts for solving inverse problems. Application to the change-point problem Submitted (2003)

[26] Li,Y. & Zhu,J.: Analysis of array CGH data for cancer studies using fused quantile regression. Bioinformatics 23 (2007) 2470-2476

[27] Lipson,D. et al.: Efficient calculation of interval scores for DNA copy number data analysis. J. Comput. Biol. 13 (2006) 215–228

[28] Lockwood,W.W.: Recent advances in array comparative genomic hybridization technologies and their applications in human genetics. European Journal of Human Genetics 14 (2006) 139–148

[29] Marioni,J.C. et al.: BioHMM: a heterogeneous hidden Markov model for segmenting

array CGH data. Bioinformatics 22 (2006) 1144-1146

[30] Myers,C.L. et al.: Accurate detection of aneuploidies in array CGH and gene expression microarray data. Bioinformatics 20 (2004) 3533-3543

[31] Nakao,K. et al.: High resolution analysis of DNA copy number alterations in colorectal cancer by array-based comparative genomic hybridization. Carcinogenesis 25 (2004) 1345-1357

[32] Natrajan,R. et al.: Blastemal Expression of Type I Insulin-Like Growth Factor Receptor in Wilms' Tumors Is Driven by Increased Copy Number and Correlates with Relapse. Cancer Res 66 (2006) 11148 - 11155

[33] Olshen, A. B. et al.: Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5 (2004) 557–572

[34] Phillips, J.L. et al.: The consequences of chromosomal aneuploidy on gene expression profiles in a cell line model for prostate carcinogenesis. Cancer Research 61 (2001) 8143-8149

[35] Picard,F. et al.: A statistical approach for array CGH data analysis. BMC Bioinformatics 6 (2005) 14 p

[36] Picard,F. et al.: A segmentation/clustering model for the analysis of array CGH data. Biometrics 63 (2007) 758-766

[37] Picard,F. et al.: Joint segmentation of multivariate Gaussian processes using mixed linear models. Submitted (2008)

[38] Pinkel,D. et al.: High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet. 20 (1998) 207–211

[39] Pollack,J.R. et al.: Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc. Natl Acad. Sci. USA 99 (2002), 12963–12968.

[40] Polzehl,J. and Spokoiny,S. Adaptive weights smoothing with applications to image restoration. J. R. Stat. Soc., Ser. B, 62 (2000) 335–354

[41] Rabiner,L.R.: A tutorial on hidden markov models and selected applications in speech recognition. In: Proceedings of the IEEE 77 (1989) 257–285

[42] Rouveirol,C. et al.: Computation of recurrent minimal genomic alterations from array-CGH data. Bioinformatics 22 (2006) 849–856

[43] Rueda,O.M. and Díaz-Uriarte,R.: Flexible and accurate detection of genomic copy-number changes from aCGH. PLoS Computational Biology 3 (2007) e122

[44] Schimek,M.G.: A roughness penalty regression approach for statistical graphics. In Edwards,D. & Raun, E. (ed.) COMPSTAT 1988. Proceedings in Computational Statistics, Heidelberg: Physica (1988) 37-43

[45] Schwarz,G.: Estimating the dimension of a model. Annals of Statistics 6 (1978) 461-464

[46] Shah,S.P. et al.: Integrating copy number polymorphisms into array CGH analysis using a robust HMM. Bioinformatics 22 (2006) 431–439

[47] Shah,S.P. et al.: Modeling recurrent DNA copy number alterations in array CGH data. Bioinformatics 23(2007) i450-i458

[48] Solinas-Toldo,S. et al.: Matrix-based comparative genomic hybridization: biochips to screen for genomic imbalances. Genes Chromosomes Cancer 20 (1997) 399–407

[49] Stjernqvist,S. et al.: Continuous-index hidden Markov modelling of array CGH copy number data. Bioinformatics 23 (2007) 1006–1014

[50] Tirkkonen,M. et al.: Distinct Somatic Genetic Changes Associated with Tumor Progression in Carriers of BRCA1 and BRCA2 Germ-line Mutations. Cancer Research 57 (1997) 1222-1227

[51] van de Wiel,M.A. et al.: CGHcall: calling aberrations for array CGH tumor profiles. Bioinformatics 23 (2007) 892–894

[52] Ventakraman,E.S. & Olshen,A.B.: A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23 (2007) 657–663

[53] Wang,P. et al.: A method for calling gains and losses in array CGH data. Biostatistics 6 (2005) 45–58

[54] Whittaker,E.: On a new method of graduation. Proc. Edinburgh Math. Soc. 41 (1923) 63-75

[55] Willenbrock,H. and Fridlyand,J.: A comparison study: applying segmentation to array CGH data for downstream analyses. Bioinformatics 21 (2005) 4084–4091

[56] Yu,T. et al.: A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array. BMC Bioinformatics 8 (2007) 11 p