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@article{pierre-jean14performance,
  title={A performance evaluation framework of {DNA} copy number analysis methods in cancer studies; application to {SNP} array data segmentation methods},
  author={Pierre-Jean, M and Rigaill, G J and Neuvial, P},
  journal={arXiv preprint arXiv:1402.7203},
  year={2014}
}

@article{hupe04analysis,
	Author = {Hup{\'e}, Philippe and Stransky, Nicolas and Thiery, Jean-Paul and Radvanyi, Fran{\c{c}}ois and Barillot, Emmanuel},
	Date-Added = {2014-02-05 14:34:11 +0000},
	Date-Modified = {2014-02-05 14:34:16 +0000},
	Journal = {Bioinformatics},
	Number = {18},
	Pages = {3413--3422},
	Publisher = {Oxford Univ Press},
	Title = {{Analysis of array-CGH data: from signal ratio to gain and loss of DNA regions}},
	Volume = {20},
	Year = {2004}}



@article{ben-yaacov08fast,
	Author = {Ben-Yaacov, Erez and Eldar, Yonina C},
	Journal = {Bioinformatics},
	Month = aug,
	Number = {16},
	Pages = {i139----i145},
	Title = {{A fast and flexible method for the segmentation of aCGH data.}},
	Volume = {24},
	Year = {2008}}


@article{rancoita10integrated,
	Author = {Rancoita, Paola MV and Hutter, Marcus and Bertoni, Francesco and Kwee, Ivo},
	Date-Added = {2014-02-05 15:01:50 +0000},
	Date-Modified = {2014-02-05 15:01:54 +0000},
	Journal = {BMC bioinformatics},
	Number = {1},
	Pages = {321},
	Publisher = {BioMed Central Ltd},
	Title = {{An integrated Bayesian analysis of LOH and copy number data}},
	Volume = {11},
	Year = {2010}}

@article{rigaill13learning,
	Author = {Rigaill, Guillem and Hocking, Toby Dylan and Bach, Francis and Vert, Jean-Philippe},
	Date-Added = {2014-01-13 09:31:59 +0000},
	Date-Modified = {2014-01-13 09:35:03 +0000},
	Journal = {{Proceedings of the 30th International Conference on Machine Learning, \emph{JMLR W\&CP}}},
	Keywords = {jointSeg},
	Number = {3},
	Pages = {172--180},
	Title = {Learning Sparse Penalties for Change-Point Detection using Max Margin Interval Regression},
	Volume = {28},
	Year = {2013},
}

@article{zhang07modified,
	Author = {Zhang, Nancy R and Siegmund, David O},
	Journal = {Biometrics},
	Keywords = {jointSeg},
	Number = {1},
	Pages = {22--32},
	Publisher = {Blackwell Synergy},
	Title = {A Modified {Bayes Information Criterion} with Applications to the Analysis of Comparative Genomic Hybridization Data},
	Volume = {63},
	Year = {2007}}

@misc{illumina-inc06illuminas,
	Author = {{Illumina, inc}},
	Howpublished = {White paper},
	Keywords = {jointSeg},
	Title = {{Illumina's Genotyping Data Normalization Methods}},
	Year = {2006}}

@misc{illumina-inc07genotyping,
	Author = {{Illumina, inc}},
	Howpublished = {User Guide},
	Keywords = {Illumina,genotyping,manual,jointSeg},
	Title = {BeadStudio Genotyping Module v3.2}},
	Url = {https://icom.illumina.com/icom/software.ilmn?id=217},
	Year = {2007},
	Bdsk-Url-1 = {https://icom.illumina.com/icom/software.ilmn?id=217}}

@article{peiffer06high-resolution,
	Abstract = {Array-CGH is a powerful tool for the detection of
chromosomal aberrations.
The introduction of high-density SNP genotyping technology to genomic
profiling, termed SNP-CGH, represents a further advance, since simultaneous
measurement of both signal intensity variations and changes in allelic
composition makes it possible to detect both copy number changes
and copy-neutral loss-of-heterozygosity (LOH) events. We demonstrate
the utility of SNP-CGH with two Infinium whole-genome genotyping
BeadChips, assaying 109,000 and 317,000 SNP loci, to detect chromosomal
aberrations in samples bearing constitutional aberrations as well
tumor samples at sub-100 kb effective resolution. Detected aberrations
include homozygous deletions, hemizygous deletions, copy-neutral
LOH, duplications, and amplifications. The statistical ability to
detect common aberrations was modeled by analysis of an X chromosome
titration model system, and sensitivity was modeled by titration
of gDNA from a tumor cell with that of its paired normal cell line.
Analysis was facilitated by using a genome browser that plots log
ratios of normalized intensities and allelic ratios along the chromosomes.
We developed two modes of SNP-CGH analysis, a single sample and a
paired sample mode. The single sample mode computes log intensity
ratios and allelic ratios by referencing to canonical genotype clusters
generated from approximately 120 reference samples, whereas the paired
sample mode uses a paired normal reference sample from the same individual.
Finally, the two analysis modes are compared and contrasted for their
utility in analyzing different types of input gDNA: low input amounts,
fragmented gDNA, and Phi29 whole-genome pre-amplified DNA.},
	Author = {Peiffer, Daniel A and Le, Jennie M and Steemers, Frank J and Chang, Weihua and Jenniges, Tony and Garcia, Francisco and Haden, Kirt and Li, Jiangzhen and Shaw, Chad A and Belmont, John and Cheung, Sau Wai and Shen, Richard M and Barker, David L and Gunderson, Kevin L},
	Date-Added = {2013-05-31 08:45:19 +0000},
	Date-Modified = {2013-05-31 08:45:19 +0000},
	Doi = {10.1101/gr.5402306},
	Journal = {Genome Res.},
	Keywords = {TumorBoost,genotyping microarrays, jointSeg},
	Month = sep,
	Number = {9},
	Pages = {1136--1148},
	Pmid = {16899659},
	Title = {High-resolution genomic profiling of chromosomal aberrations using {Infinium} whole-genome genotyping},
	Url = {http://dx.doi.org/10.1101/gr.5402306},
	Volume = {16},
	Year = {2006},
	Bdsk-Url-1 = {http://dx.doi.org/10.1101/gr.5402306}}

@article{greenman10picnic,
	Author = {Greenman, Chris D and Bignell, Graham and Butler, Adam and Edkins, Sarah and Hinton, Jon and Beare, Dave and Swamy, Sajani and Santarius, Thomas and Chen, Lina and Widaa, Sara and Futreal, P Andy and Stratton, Michael R},
	Journal = {Biostatistics},
	Number = {1},
	Pages = {164--175},
	Title = {{PICNIC}: an algorithm to predict absolute allelic copy number variation with microarray cancer data},
	Volume = {11},
	Year = {2010}}

@article{sun09integrated,
	Author = {Sun, Wei and Wright, Fred A and Tang, Zhengzheng and Nordgard, Silje H and Loo, Peter Van and Yu, Tianwei and Kristensen, Vessela N and Perou, Charles M},
	Journal = {Nucl. Acids Res.},
	Month = sep,
	Number = {16},
	Pages = {5365--5377},
	Title = {{Integrated study of copy number states and genotype calls using high-density SNP arrays}},
	Volume = {37},
	Year = {2009}}

@article{lai05comparative,
	Abstract = {MOTIVATION: Array Comparative Genomic Hybridization (CGH) can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. While a large number of approaches have been proposed for analyzing the large array CGH datasets, the relative merits of these methods in practice are not clear. RESULTS: We compare 11 different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on diverse techniques such as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets and genetic algorithms. We compute the Receiver Operating Characteristic (ROC) curves using simulated data to quantify sensitivity and specificity for various levels of signal-to-noise ratio and different sizes of abnormalities. We also characterize their performance on chromosomal regions of interest in a real dataset obtained from patients with Glioblastoma Multiforme. While comparisons of this type are difficult due to possibly sub-optimal choice of parameters in the methods, they nevertheless reveal general characteristics that are helpful to the biological investigator.},
	Author = {Lai, Weil R and Johnson, Mark D and Kucherlapati, Raju and Park, Peter J},
	Date-Added = {2013-04-24 15:14:05 +0000},
	Date-Modified = {2013-04-24 15:14:05 +0000},
	Doi = {10.1093/bioinformatics/bti611},
	File = {Users/pierre/Documents/Biblio/lai05comparative.pdf},
	Issn = {1367-4803},
	Journal = {Bioinformatics (Oxford, England)},
	Keywords = {tianjin},
	Month = oct,
	Number = {19},
	Pages = {3763--70},
	Pmid = {16081473},
	Title = {{Comparative analysis of algorithms for identifying amplifications and deletions in array-CGH data.}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/16081473},
	Volume = {21},
	Year = {2005},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/16081473},
	Bdsk-Url-2 = {http://dx.doi.org/10.1093/bioinformatics/bti611}}

@article{mosen-ansorena12comparison,
	Author = {Mos{\'e}n-Ansorena, David and Aransay, Ana and Rodr{\'\i}guez-Ezpeleta, Naiara},
	Date-Added = {2013-04-24 15:12:44 +0000},
	Date-Modified = {2013-04-24 15:13:00 +0000},
	Journal = {BMC bioinformatics},
	Number = {1},
	Pages = {192},
	Publisher = {BioMed Central Ltd},
	Title = {Comparison of methods to detect copy number alterations in cancer using simulated and real genotyping data},
	Volume = {13},
	Year = {2012}}

@article{willenbrock05a-comparison,
	Abstract = {Array comparative genomic hybridization (CGH) allows detection and mapping of copy number of DNA segments. A challenge is to make inferences about the copy number structure of the genome. Several statistical methods have been proposed to determine genomic segments with different copy number levels. However, to date, no comprehensive comparison of various characteristics of these methods exists. Moreover, the segmentation results have not been utilized in downstream analyses.},
	Author = {Willenbrock, Hanni and Fridlyand, Jane},
	Date-Added = {2013-04-24 15:09:27 +0000},
	Date-Modified = {2013-04-25 12:57:22 +0000},
	Doi = {10.1093/bioinformatics/bti677},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Algorithms; Carcinoma, Squamous Cell; Computational Biology; Computer Simulation; DNA; Data Interpretation, Statistical; Databases, Genetic; Gene Expression Regulation, Neoplastic; Gene Library; Genetic Techniques; Genome; Humans; Internet; Mouth Neoplasms; Nucleic Acid Hybridization; Sequence Analysis, DNA; Software},
	Month = {Nov},
	Number = {22},
	Pages = {4084-91},
	Pmid = {16159913},
	Pst = {ppublish},
	Title = {A comparison study: applying segmentation to array-{CGH} data for downstream analyses},
	Volume = {21},
	Year = {2005},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/bti677}}

@article{fridlyand04hidden,
	Author = {Fridlyand, J},
	Date-Added = {2013-04-24 10:06:08 +0000},
	Date-Modified = {2013-04-24 10:06:08 +0000},
	Doi = {10.1016/j.jmva.2004.02.008},
	File = {:Users/pierre/Documents/Biblio/fridlyand04application.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {array cgh,cancer,chromosomal aberrations,genomic profile,hmm,mmr,mutation},
	Month = jul,
	Number = {1},
	Pages = {132--153},
	Title = {{Hidden Markov models approach to the analysis of array-CGH data}},
	Url = {http://linkinghub.elsevier.com/retrieve/pii/S0047259X04000260},
	Volume = {90},
	Year = {2004},
	Bdsk-Url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0047259X04000260},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.jmva.2004.02.008}}

@article{bellman61approximation,
	Author = {Bellman, Richard},
	Date-Added = {2013-03-15 09:01:38 +0000},
	Date-Modified = {2013-03-15 09:01:43 +0000},
	Journal = {Communications of the ACM},
	Number = {6},
	Pages = {284},
	Publisher = {ACM},
	Title = {On the approximation of curves by line segments using dynamic programming},
	Volume = {4},
	Year = {1961}}

@article{sen75tests,
	Author = {Sen, A. and Srivastava, M.S.},
	Date-Added = {2013-01-06 23:57:13 +0000},
	Date-Modified = {2013-01-06 23:57:13 +0000},
	Journal = {The Annals of Statistics},
	Keywords = {jointSeg},
	Number = {1},
	Pages = {98--108},
	Publisher = {Institute of Mathematical Statistics},
	Title = {On tests for detecting change in mean},
	Volume = {3},
	Year = {1975}}

@article{tibshirani05sparsity,
	Author = {Tibshirani, R and Saunders, M and Rosset, S and Zhu, J and Knight, K},
	Date-Added = {2013-01-06 21:57:30 +0000},
	Date-Modified = {2013-01-06 21:57:30 +0000},
	File = {:Users/pierre/Documents/Biblio/tibshirani05sparsity.pdf:pdf},
	Journal = {Journal of the Royal Statistical Society. Series B, Statistical Methodology},
	Keywords = {jointSeg},
	Pages = {91--108},
	Publisher = {Blackwell Publishers},
	Title = {{Sparsity and smoothness via the fused lasso}},
	Year = {2005}}

@article{tibshirani08spatial,
	Author = {Tibshirani, R. and Wang, P.},
	Date-Added = {2013-01-06 21:57:30 +0000},
	Date-Modified = {2013-01-06 21:57:30 +0000},
	Journal = {Biostatistics},
	Keywords = {jointSeg},
	Number = {1},
	Pages = {18--29},
	Publisher = {Biometrika Trust},
	Title = {{Spatial smoothing and hot spot detection for CGH data using the fused lasso}},
	Volume = {9},
	Year = {2008}}

@incollection{zhang10dna-copy,
	Author = {Zhang, N.R.},
	Booktitle = {Frontiers in Computational and Systems Biology},
	Date-Added = {2013-01-06 21:50:52 +0000},
	Date-Modified = {2013-01-06 21:50:52 +0000},
	Editor = {Feng, Jianfeng and Fu, Wenjiang and Sun, Fengzhu},
	Keywords = {tianjin,jointSeg},
	Pages = {259--281},
	Publisher = {Springer-Verlag},
	Title = {{DNA} Copy Number Profiling in Normal and Tumor Genomes},
	Year = {2010}}

@book{breiman84classification,
	Author = {Breiman, L. and Friedman, J. and Stone, C.J. and Olshen, R.A.},
	Date-Added = {2013-01-06 21:32:39 +0000},
	Date-Modified = {2013-01-06 21:32:39 +0000},
	Keywords = {jointSeg},
	Publisher = {Chapman \& Hall/CRC},
	Title = {Classification and regression trees},
	Year = {1984}}

@article{olshen04circular,
	Author = {Olshen, A B and Venkatraman, E S and Lucito, Robert and Wigler, Michael},
	Date-Added = {2013-01-06 21:21:22 +0000},
	Date-Modified = {2013-01-06 21:21:22 +0000},
	Journal = {Biostatistics},
	Keywords = {CBS, tianjin},
	Number = {4},
	Pages = {557--572},
	Title = {Circular binary segmentation for the analysis of array-based {DNA} copy number data},
	Volume = {5},
	Year = {2004}}

@article{venkatraman07a-faster,
	Abstract = {MOTIVATION: Array CGH technologies enable the simultaneous measurement of DNA copy number for thousands of sites on a genome. We developed the circular binary segmentation (CBS) algorithm to divide the genome into regions of equal copy number. The algorithm tests for change-points using a maximal t-statistic with a permutation reference distribution to obtain the corresponding P-value. The number of computations required for the maximal test statistic is O(N2), where N is the number of markers. This makes the full permutation approach computationally prohibitive for the newer arrays that contain tens of thousands markers and highlights the need for a faster algorithm. RESULTS: We present a hybrid approach to obtain the P-value of the test statistic in linear time. We also introduce a rule for stopping early when there is strong evidence for the presence of a change. We show through simulations that the hybrid approach provides a substantial gain in speed with only a negligible loss in accuracy and that the stopping rule further increases speed. We also present the analyses of array CGH data from breast cancer cell lines to show the impact of the new approaches on the analysis of real data. AVAILABILITY: An R version of the CBS algorithm has been implemented in the "DNAcopy" package of the Bioconductor project. The proposed hybrid method for the P-value is available in version 1.2.1 or higher and the stopping rule for declaring a change early is available in version 1.5.1 or higher.},
	Address = {Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10021, USA. venkatre@mskcc.org},
	Author = {Venkatraman, E S and Olshen, Adam B},
	Date-Added = {2013-01-06 21:21:22 +0000},
	Date-Modified = {2013-01-06 21:21:22 +0000},
	Doi = {10.1093/bioinformatics/btl646},
	File = {:Users/pierre/Documents/Biblio/657.pdf:pdf},
	Issn = {1460-2059 (Electronic)},
	Journal = {Bioinformatics},
	Keywords = {segmentation,cgh,tianjin},
	Month = mar,
	Number = {6},
	Pages = {657--663},
	Pmid = {17234643},
	Title = {{A faster circular binary segmentation algorithm for the analysis of array-CGH data}},
	Volume = {23},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btl646}}

@article{zhang08detecting,
	Abstract = {We discuss the detection of local signals that occur at the same location in multiple one-dimensional noisy sequences, with particular attention to relatively weak signals that may occur in only a fraction of the sequences. We propose simple scan and segmentation algorithms based on the sum of the chi-squared statistics for each individual sample, which is equivalent to the generalized likelihood ratio for a model where the errors in each sample are independent. The simple geometry of the statistic allows us to derive accurate analytic approximations to the significance level of such scans. The formulation of the model is motivated by the biological problem of detecting recurrent DNA copy number variants in multiple samples. We show using replicates and parent-child comparisons that pooling data across samples results in more accurate detection of copy number variants. We also apply the multisample segmentation algorithm to the analysis of a cohort of tumour samples containing complex nested and overlapping copy number aberrations, for which our method gives a sparse and intuitive cross-sample summary.},
	Author = {Zhang, Nancy R. and Siegmund, David O. and Ji, Hanlee and Li, Jun Z.},
	Date-Added = {2013-01-06 21:05:32 +0000},
	Date-Modified = {2013-01-06 21:05:32 +0000},
	Doi = {10.1093/biomet/asq025},
	Eprint = {http://biomet.oxfordjournals.org/content/97/3/631.full.pdf+html},
	Journal = {Biometrika},
	Keywords = {jointSeg},
	Number = {3},
	Pages = {631-645},
	Title = {Detecting simultaneous changepoints in multiple sequences},
	Url = {http://biomet.oxfordjournals.org/content/97/3/631.abstract},
	Volume = {97},
	Year = {2010},
	Bdsk-File-1 = {YnBsaXN0MDDUAQIDBAUGJCVYJHZlcnNpb25YJG9iamVjdHNZJGFyY2hpdmVyVCR0b3ASAAGGoKgHCBMUFRYaIVUkbnVsbNMJCgsMDxJXTlMua2V5c1pOUy5vYmplY3RzViRjbGFzc6INDoACgAOiEBGABIAFgAdccmVsYXRpdmVQYXRoWWFsaWFzRGF0YV8QJy4uLy4uLy4uLy4uL0JpYmxpby96aGFuZzA4ZGV0ZWN0aW5nLnBkZtIXCxgZV05TLmRhdGFPEQGqAAAAAAGqAAIAAAxNYWNpbnRvc2ggSEQAAAAAAAAAAAAAAAAAAADMMS8SSCsAAAAIyNIUemhhbmcwOGRldGVjdGluZy5wZGYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGKnf80J/OUAAAAAAAAAAAAEAAIAAAkgAAAAAAAAAAAAAAAAAAAABkJpYmxpbwAQAAgAAMwxEvIAAAARAAgAAM0J7tUAAAABABAACMjSAAXB0gAFwdEAAg35AAIARU1hY2ludG9zaCBIRDpVc2VyczoAcG5ldXZpYWw6AERvY3VtZW50czoAQmlibGlvOgB6aGFuZzA4ZGV0ZWN0aW5nLnBkZgAADgAqABQAegBoAGEAbgBnADAAOABkAGUAdABlAGMAdABpAG4AZwAuAHAAZABmAA8AGgAMAE0AYQBjAGkAbgB0AG8AcwBoACAASABEABIANFVzZXJzL3BuZXV2aWFsL0RvY3VtZW50cy9CaWJsaW8vemhhbmcwOGRldGVjdGluZy5wZGYAEwABLwAAFQACAA///wAAgAbSGxwdHlokY2xhc3NuYW1lWCRjbGFzc2VzXU5TTXV0YWJsZURhdGGjHR8gVk5TRGF0YVhOU09iamVjdNIbHCIjXE5TRGljdGlvbmFyeaIiIF8QD05TS2V5ZWRBcmNoaXZlctEmJ1Ryb290gAEACAARABoAIwAtADIANwBAAEYATQBVAGAAZwBqAGwAbgBxAHMAdQB3AIQAjgC4AL0AxQJzAnUCegKFAo4CnAKgAqcCsAK1AsICxQLXAtoC3wAAAAAAAAIBAAAAAAAAACgAAAAAAAAAAAAAAAAAAALh}}

@article{neumann41the-mean,
	Author = {von Neumann, J. and Kent, R. H. and Bellinson, H. R. and Hart, B. I.},
	Copyright = {Copyright 1941 Institute of Mathematical Statistics},
	Date-Added = {2013-01-06 17:41:41 +0000},
	Date-Modified = {2013-01-06 17:41:41 +0000},
	File = {:NeumannJ_etal_1941-The Mean Square Successive Difference.pdf:PDF},
	Issn = {00034851},
	Journal = {The Annals of Mathematical Statistics},
	Jstor_Articletype = {primary_article},
	Jstor_Formatteddate = {Jun., 1941},
	Keywords = {tianjin,jointSeg},
	Number = {2},
	Pages = {153--162},
	Publisher = {Institute of Mathematical Statistics},
	Title = {The Mean Square Successive Difference},
	Url = {http://www.jstor.org/stable/2235765},
	Volume = {12},
	Year = {1941},
	Bdsk-Url-1 = {http://www.jstor.org/stable/2235765}}

@article{picard05a-statistical,
	Abstract = {BACKGROUND: Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise: to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. RESULTS: We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. CONCLUSIONS: Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome.},
	Author = {Picard, Franck and Robin, Stephane and Lavielle, Marc and Vaisse, Christian and Daudin, Jean-Jacques},
	Date-Added = {2013-01-06 17:41:10 +0000},
	Date-Modified = {2013-01-06 17:41:10 +0000},
	Doi = {10.1186/1471-2105-6-27},
	File = {:Users/pierre/Documents/Biblio/picard05a statistical.pdf:pdf},
	Issn = {1471-2105},
	Journal = {BMC bioinformatics},
	Keywords = {jointSeg},
	Month = jan,
	Pages = {27},
	Pmid = {15705208},
	Title = {{A statistical approach for array-CGH data analysis.}},
	Url = {http://www.ncbi.nlm.nih.gov/pubmed/15705208},
	Volume = {6},
	Year = {2005},
	Bdsk-Url-1 = {http://www.ncbi.nlm.nih.gov/pubmed/15705208},
	Bdsk-Url-2 = {http://dx.doi.org/10.1186/1471-2105-6-27}}

@techreport{rigaill10pruned,
	Abstract = {Multiple change-point detection models assume that the observed data is a realization of an independent random process affected by K-1 abrupt changes, called change-points, at some unknown positions. For off-line detection a dynamic programming (DP) algorithm retrieves the K-1 change-points minimizing the quadratic loss and reduces the complexity from $\Theta$(n\^{}K) to $\Theta$(Kn\^{}2) where n is the number of observations. The quadratic complexity in n still restricts the use of such an algorithm to small or intermediate values of n. We propose a pruned DP algorithm that recovers the optimal solution. We demonstrate that at worst the complexity is in O(Kn\^{}2) time and O(Kn) space and is therefore at worst equivalent to the classical DP algorithm. We show empirically that the run-time of our proposed algorithm is drastically reduced compared to the classical DP algorithm. More precisely, our algorithm is able to process a million points in a matter of minutes compared to several days with the classical DP algorithm. Moreover, the principle of the proposed algorithm can be extended to other convex losses (for example the Poisson loss) and as the algorithm process one observation after the other it could be adapted for on-line problems.},
	Author = {Rigaill, Guillem},
	Date-Added = {2013-01-06 17:40:35 +0000},
	Date-Modified = {2013-01-06 17:40:35 +0000},
	File = {:Users/pierre/Documents/Biblio/rigaill10pruned.pdf:pdf},
	Institution = {http://arXiv.org/abs/1004.0887},
	Keywords = {segmentation,jointSeg},
	Title = {{Pruned dynamic programming for optimal multiple change-point detection}},
	Year = {2010}}

@article{edgar02gene,
	Author = {Edgar, R. and Domrachev, M. and Lash, A.E.},
	Journal = {Nucleic acids research},
	Number = {1},
	Pages = {207--210},
	Title = {{G}ene {E}xpression {O}mnibus: {NCBI} gene expression and hybridization array data repository},
	Volume = {30},
	Year = {2002}}

@article{harchaoui08catching,
	Author = {Harchaoui, Z and L\'{e}vy-Leduc, C},
	Journal = {Advances in Neural Information Processing Systems},
	Pages = {161--168},
	Title = {{Catching change-points with lasso}},
	Volume = {20},
	Year = {2008}}

@article{bondell08simultaneous,
	Author = {Bondell, H.D. and Reich, B.J.},
	Journal = {Biometrics},
	Number = {1},
	Pages = {115--123},
	Title = {Simultaneous regression shrinkage, variable selection, and supervised clustering of predictors with {OSCAR}},
	Volume = {64},
	Year = {2008}}

@techreport{gey08using,
	Author = {Gey, S. and Lebarbier, E.},
	Institution = {Statistics for Systems Biology research group},
	Year = 2008,
	Title = {Using {CART} to Detect Multiple Change Points in the Mean for Large Sample}}

@article{bengtsson09a-single-array,
	Author = {Bengtsson, H and Wirapati, P and Speed, T P},
	Journal = {Bioinformatics},
	Number = {17},
	Pages = {2149--2156},
	Title = {{A single-array preprocessing method for estimating full-resolution raw copy numbers from all Affymetrix arrays including GenomeWideSNP 5 \& 6}},
	Volume = {27},
	Year = {2009}}

@article{hoefling10a-path,
	Author = {Hoefling, H.},
	Journal = {Journal of Computational and Graphical Statistics},
	Number = {4},
	Pages = {984--1006},
	Title = {A path algorithm for the fused lasso signal approximator},
	Volume = {19},
	Year = {2010}}

@article{vert10fast,
	Author = {Vert, J.-P. and Bleakley, K.},
	Journal = {Advances in Neural Information Processing Systems},
	Pages = {2343--2351},
	Title = {Fast detection of multiple change-points shared by many signals using group {LARS}},
	Volume = {23},
	Year = {2010}}

@article{zhang10reconstructing,
	Author = {Zhang, Z. and Lange, K. and Ophoff, R. and Sabatti, C.},
	Journal = {The annals of applied statistics},
	Number = {4},
	Pages = {1749},
	Title = {Reconstructing DNA copy number by penalized estimation and imputation},
	Volume = {4},
	Year = {2010}}

@article{ritz11detection,
	Author = {Ritz, Anna and Paris, Pamela and Ittmann, Michael and Collins, Colin and Raphael, Benjamin},
	Journal = {BMC Bioinformatics},
	Number = {1},
	Pages = {114},
	Title = {Detection of recurrent rearrangement breakpoints from copy number data},
	Volume = {12},
	Year = {2011}}

@article{chen11estimation,
	Author = {Chen, Hao and Xing, Haipeng and Zhang, Nancy R},
	Journal = {{PLoS} Computational Biology},
	Number = {1},
	Pages = {e1001060},
	Title = {Estimation of parent specific {DNA} copy number in tumors using high-density genotyping arrays},
	Volume = {7},
	Year = {2011}}

@article{rigaill11exact,
	Author = {Rigaill, G. and Lebarbier, E. and Robin, S.},
	Journal = {Statistics and Computing},
	Pages = {1--13},
	Title = {Exact posterior distributions and model selection criteria for multiple change-point detection problems},
	Year = {2011}}

@techreport{petry11pairwise,
	Author = {Petry, S. and Flexeder, C. and Tutz, G.},
	Institution = {University of Munich, Department of Statistics},
	Title = {Pairwise Fused Lasso},
	Year = {2011}}

@incollection{neuvial11statistical,
	Author = {Neuvial, Pierre and Bengtsson, Henrik and Speed, Terence P},
	Booktitle = {Handbook of Statistical Bioinformatics},
	Edition = {1st},
	Month = mar,
	Publisher = {Springer},
	Series = {Springer Handbooks of Computational Statistics},
	Title = {Statistical analysis of Single Nucleotide Polymorphism microarrays in cancer studies},
	Year = {2011}}

@techreport{bleakley11the-group,
	Author = {Bleakley, Kevin and Vert, Jean-Philippe},
	Institution = {http://hal.archives-ouvertes.fr/hal-00602121/en},
	Month = jun,
	Title = {{The group fused Lasso for multiple change-point detection}},
	Year = {2011}}

@article{olshen11parent-specific,
	Author = {Olshen, Adam B and Bengtsson, Henrik and Neuvial, Pierre and Spellman, Paul T and Olshen, Richard A and Seshan, Venkatraman E},
	Journal = {Bioinformatics},
	Month = {Aug},
	Number = {15},
	Pages = {2038-2046},
	Title = {Parent-specific copy number in paired tumor-normal studies using circular binary segmentation},
	Volume = {27},
	Year = {2011}}

@article{yang12simple,
	Author = {Yang, T.Y.},
	Journal = {BMC bioinformatics},
	Number = {1},
	Pages = {277},
	Title = {Simple binary segmentation frameworks for identifying variation in DNA copy number},
	Volume = {13},
	Year = {2012}}

@article{hocking13learning,
	Author = {Hocking, Toby and Schleiermacher, Gudrun and Janoueix-Lerosey, Isabelle and Boeva, Valentina and Cappo, Julie and Delattre, Olivier and Bach, Francis and Vert, Jean-Philippe},
	Date-Modified = {2014-01-13 09:21:07 +0000},
	Journal = {BMC Bioinformatics},
	Number = {1},
	Pages = {164},
	Title = {Learning smoothing models of copy number profiles using breakpoint annotations},
	Volume = {14},
	Year = {2013}}

@article{lai12change-point,
	Author = {Lai, Yinglei},
	Journal = {Journal of Computational Biology},
	Month = jun,
	Number = {6},
	Pages = {679--693},
	Title = {{Change-Point} Analysis of Paired {Allele-Specific} Copy Number Variation Data},
	Volume = {19},
	Year = {2012}}

@article{ortiz-estevez12calmate,
	Author = {{Ortiz-Estevez}, Maria and Aramburu, Ander and Bengtsson, Henrik and Neuvial, Pierre and Rubio, Angel},
	Journal = {Bioinformatics},
	Month = jul,
	Number = {13},
	Pages = {1793--1794},
	Title = {{CalMaTe:} A Method and Software to Improve {Allele-Specific} Copy Number of {SNP} Arrays for Downstream Segmentation},
	Volume = {28},
	Year = {2012}}

@misc{bengtsson:the-aroma,
	Author = {Bengtsson, Henrik and others},
	Howpublished = {http://aroma-project.org},
	Title = {{The aroma project: an open-source {R} framework for your microarray analysis}}}

@article{bengtsson10tumorboost,
	Author = {Bengtsson, Henrik and Neuvial, Pierre and Speed, Terence P},
	Journal = {BMC bioinformatics},
	Month = may,
	Number = {1},
	Pages = {245},
	Title = {{TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays.}},
	Volume = {11},
	Year = {2010}}

@article{lebarbier05detecting,
	Author = {Lebarbier, E.},
	Journal = {Signal processing},
	Number = {4},
	Pages = {717--736},
	Title = {Detecting multiple change-points in the mean of Gaussian process by model selection},
	Volume = {85},
	Year = {2005}}

@article{ramakrishna10identification,
	Author = {Ramakrishna, Manasa and Williams, Louise H and Boyle, Samantha E and Bearfoot, Jennifer L and Sridhar, Anita and Speed, Terence P and Gorringe, Kylie L and Campbell, Ian G},
	Journal = {PloS one},
	Month = jan,
	Number = {4},
	Pages = {e9983},
	Title = {{Identification of candidate growth promoting genes in ovarian cancer through integrated copy number and expression analysis.}},
	Volume = {5},
	Year = {2010}}

@article{rasmussen11allele,
	Author = {Rasmussen, M and Sundstr{\"o}m, M and G{\"o}ransson Kultima, H and Botling, J and et al.},
	Journal = {Genome Biol},
	Month = oct,
	Number = {10},
	Pages = {R108},
	Title = {{Allele-specific copy number analysis of tumor samples with aneuploidy and tumor heterogeneity.}},
	Volume = {12},
	Year = {2011}}

@article{staaf08segmentation,
	Author = {Staaf, J and Lindgren, D and Vallon-Christersson, J and Isaksson, A and et al.},
	Journal = {Genome Biol},
	Month = oct,
	Number = {9},
	Pages = {R136},
	Title = {{Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays.}},
	Volume = {9},
	Year = {2008}}

@article{killick11optimal,
	Author = {Killick, R. and Fearnhead, P. and Eckley, IA},
	Journal = {Journal of the American Statistical Association},
	Title = {Optimal detection of changepoints with a linear computational cost},
        Pages = {1590-1598},
        Number = 107,
        Volume = 500,
	Year = {2012}}

@article{BirgeMassartSelectionModel2001,
	Author = {Birg\'e, Lucien and Massart, Pascal},
	Doi = {10.1007/s100970100031},
	Issn = {1435-9855},
	Issue = {3},
	Journal = {Journal of the European Mathematical Society},
	Keywords = {Mathematics Subject Classification (1991): 62G07, 62C20, 41A46},
	Language = {English},
	Pages = {203-268},
	Publisher = {Springer-Verlag},
	Title = {Gaussian model selection},
	Url = {http://dx.doi.org/10.1007/s100970100031},
	Volume = {3},
	Year = {2001},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/s100970100031}}

@article{MosenBivariate2013,
	Abstract = {BACKGROUND:SNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation.RESULTS:In the present work, we introduce a generic framework for bivariate segmentation of SNP array data for ASCN analysis. For the matter, we discuss the characteristics of the typically applied BAF transformation and how they affect segmentation, introduce concepts of multivariate time series analysis that are of concern in this field and discuss the appropriate formulation of the problem. The framework is implemented in a method named CnaStruct, the bivariate form of the structural change model (SCM), which has been successfully applied to transcriptome mapping and aCGH.CONCLUSIONS:On a comprehensive synthetic dataset, we show that CnaStruct outperforms the segmentation of existing ASCN analysis methods. Furthermore, CnaStruct can be integrated into the workflows of several ASCN analysis tools in order to improve their performance, specially on tumour samples highly contaminated by normal cells.},
	Author = {Mosen-Ansorena, David and Aransay, Ana Maria},
	Doi = {10.1186/1471-2105-14-84},
	Issn = {1471-2105},
	Journal = {BMC Bioinformatics},
	Number = {1},
	Pages = {84},
	Title = {{Bivariate segmentation of SNP-array data for allele-specific copy number analysis in tumour samples}},
	Url = {http://www.biomedcentral.com/1471-2105/14/84},
	Volume = {14},
	Year = {2013},
	Bdsk-Url-1 = {http://www.biomedcentral.com/1471-2105/14/84},
	Bdsk-Url-2 = {http://dx.doi.org/10.1186/1471-2105-14-84}}

@article{ZhangReconstructing2012,
	Author = {Zhang, Zhongyang and Lange, Kenneth and Sabatti, Chiara},
	Doi = {10.1186/1471-2105-13-205},
	Issn = {1471-2105},
	Journal = {BMC Bioinformatics},
	Number = {1},
	Pages = {205},
	Pubmedid = {22897923},
	Title = {Reconstructing DNA copy number by joint segmentation of multiple sequences},
	Url = {http://www.biomedcentral.com/1471-2105/13/205},
	Volume = {13},
	Year = {2012},
	Bdsk-Url-1 = {http://www.biomedcentral.com/1471-2105/13/205},
	Bdsk-Url-2 = {http://dx.doi.org/10.1186/1471-2105-13-205}}

@article{KillickOptimal2011,
	Adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	Adsurl = {http://adsabs.harvard.edu/abs/2011arXiv1101.1438K},
	Archiveprefix = {arXiv},
	Author = {{Killick}, R. and {Fearnhead}, P. and {Eckley}, I.~A.},
	Eprint = {1101.1438},
	Journal = {ArXiv e-prints},
	Keywords = {Statistics - Methodology, Quantitative Biology - Genomics, Quantitative Biology - Quantitative Methods},
	Month = jan,
	Primaryclass = {stat.ME},
	Title = {{Optimal detection of changepoints with a linear computational cost}},
	Year = 2011}


@article{hanahan11hallmarks,
  title={Hallmarks of cancer: the next generation},
  author={Hanahan, Douglas and Weinberg, Robert A},
  journal={Cell},
  volume={144},
  number={5},
  pages={646--674},
  year={2011},
  publisher={Elsevier}
}


@article{tcga11integrated,
	Author = {{Research Network}, {The Cancer Genome Atlas}},
	Journal = {Nature},
	Month = {jun},
	Number = {7353},
	Pages = {609--615},
	Title = {Integrated Genomic Analyses of Ovarian Carcinoma},
	Volume = {474},
	Year = {2011}}
