@Manual{haar-package,
    title = {HaarSeg: HaarSeg},
    author = {Erez Ben-Yaacov and Yonina C. Eldar},
    year = {2009},
    note = {R package version 0.0.3/r4},
    url = {http://R-Forge.R-project.org/projects/haarseg/},
  }

@Manual{Rcitation,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2013},
  url = {http://www.R-project.org/},
}


@article{Rueda2010a,
author = {Rueda, Oscar M and Diaz-Uriarte, Ramon},
file = {:home/ramon/Mendeley-pdfs/Finding Recurrent Copy Number Alteration Regions A Review of Methods/2010/Current Bioinformatics/Finding Recurrent Copy Number Alteration Regions A Review of Methods - 2010 - Current Bioinformatics.pdf:pdf},
journal = {Current Bioinformatics},
keywords = {acgh,common regions,copy number alterations,recurrent},
pages = {1--17},
title = {{Finding Recurrent Copy Number Alteration Regions : A Review of Methods}},
volume = {5},
year = {2010}
}

@article{VandeWiel2007,
abstract = {An algorithm to reduce multi-sample array CGH data from thousands of clones to tens or hundreds of clone regions is introduced. This reduction of the data is performed such that little information is lost, which is possible due to the high dependencies between neighboring clones. The algorithm is explained using a small example. The potential beneficial effects of the algorithm for downstream analysis are illustrated by re-analysis of previously published colorectal cancer data. Using multiple testing corrections suitable for these data, we provide statistical evidence for genomic differences on several clone regions between MSI+ and CIN+ tumors. The algorithm, named CGHregions, is available as an easy-to-use script in R.},
author = {van de Wiel, Mark A and van Wieringen, Wessel N},
file = {:home/ramon/Mendeley-pdfs/CGHregions Dimension Reduction for Array CGH Data with Minimal Information Loss./2007/Cancer informatics/CGHregions Dimension Reduction for Array CGH Data with Minimal Information Loss. - 2007 - Cancer informatics.pdf:pdf},
issn = {1176-9351},
journal = {Cancer informatics},
keywords = {array cgh,dimension reduction,fdr,statistical testing,tumor profiles},
month = jan,
number = {0},
pages = {55--63},
pmid = {19455235},
title = {{CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2675846\&tool=pmcentrez\&rendertype=abstract},
volume = {3},
year = {2007}
}

  @Manual{aCGHpackage,
    title = {aCGH: Classes and functions for Array Comparative Genomic
Hybridization data.},
    author = {Jane Fridlyand and Peter Dimitrov},
    year = {2010},
    note = {R package version 1.41.2},
  }

  @Manual{snapCGH,
    title = {snapCGH: Segmentation, normalisation and processing of aCGH data.},
    author = {Mike L. Smith and John C. Marioni and Steven McKinney and Thomas Hardcastle and Natalie P. Thorne},
    year = {2009},
    note = {R package version 1.33.0},
  }


@article{hmm-fridlyand,
author = {Fridlyand, Jane   and Snijders, Antoine  M.  and Pinkel, Dan   and
Albertson, Donna  G.},
journal = {Journal of Multivariate Analysis},
pages = {132--153},
title = {{Hidden Markov models approach to the analysis of array CGH data. }},
volume = {90},
year = {2004}
}





@Manual{CGHregions-manual,
    title = {CGHregions: Dimension Reduction for Array CGH Data with Minimal Information Loss.},
    author = {Vosse, Sjoerd and van de Wiel, Mark},
    year = {2009},
    note = {R package version 1.7.1},
  }

@article{Diaz-Uriarte2007,
abstract = {BACKGROUND: Copy number alterations (CNAs) in genomic DNA have been associated with complex human diseases, including cancer. One of the most common techniques to detect CNAs is array-based comparative genomic hybridization (aCGH). The availability of aCGH platforms and the need for identification of CNAs has resulted in a wealth of methodological studies. METHODOLOGY/PRINCIPAL FINDINGS: ADaCGH is an R package and a web-based application for the analysis of aCGH data. It implements eight methods for detection of CNAs, gains and losses of genomic DNA, including all of the best performing ones from two recent reviews (CBS, GLAD, CGHseg, HMM). For improved speed, we use parallel computing (via MPI). Additional information (GO terms, PubMed citations, KEGG and Reactome pathways) is available for individual genes, and for sets of genes with altered copy numbers. CONCLUSIONS/SIGNIFICANCE: ADACGH represents a qualitative increase in the standards of these types of applications: a) all of the best performing algorithms are included, not just one or two; b) we do not limit ourselves to providing a thin layer of CGI on top of existing BioConductor packages, but instead carefully use parallelization, examining different schemes, and are able to achieve significant decreases in user waiting time (factors up to 45x); c) we have added functionality not currently available in some methods, to adapt to recent recommendations (e.g., merging of segmentation results in wavelet-based and CGHseg algorithms); d) we incorporate redundancy, fault-tolerance and checkpointing, which are unique among web-based, parallelized applications; e) all of the code is available under open source licenses, allowing to build upon, copy, and adapt our code for other software projects.},
author = {Diaz-Uriarte, Ramon and Rueda, Oscar M},
doi = {10.1371/journal.pone.0000737},
file = {:home/ramon/Mendeley-pdfs/ADaCGH A parallelized web-based application and R package for the analysis of aCGH data./2007/PloS one/ADaCGH A parallelized web-based application and R package for the analysis of aCGH data. - 2007 - PloS one.pdf:pdf},
issn = {1932-6203},
journal = {PloS one},
keywords = {Algorithms,Comparative Genomic Hybridization,Comparative Genomic Hybridization: methods,Computational Biology,Computational Biology: methods,DNA Copy Number Variations,Gene Expression Profiling,Gene Expression Profiling: methods,Humans,Internet,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Software,Time Factors},
month = jan,
number = {1},
pages = {e737},
pmid = {17710137},
title = {{ADaCGH: A parallelized web-based application and R package for the analysis of aCGH data.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1940324\&tool=pmcentrez\&rendertype=abstract},
volume = {2},
year = {2007}
}



@article{Carro2010,
abstract = {waviCGH is a versatile web server for the analysis and comparison of genomic copy number alterations in multiple samples from any species. waviCGH processes data generated by high density SNP-arrays, array-CGH or copy-number calls generated by any technique. waviCGH includes methods for pre-processing of the data, segmentation, calling of gains and losses, and minimal common regions determination over a set of experiments. The server is a user-friendly interface to the analytical methods, with emphasis on results visualization in a genomic context. Analysis tools are introduced to the user as the different steps to follow in an experimental protocol. All the analysis steps generate high quality images and tables ready to be imported into spreadsheet programs. Additionally, for human, mouse and rat, altered regions are represented in a biological context by mapping them into chromosomes in an integrated cytogenetic browser. waviCGH is available at http://wavi.bioinfo.cnio.es.},
author = {Carro, Angel and Rico, Daniel and Rueda, Oscar M and Diaz-Uriarte, Ramon and Pisano, David G},
doi = {10.1093/nar/gkq441},
file = {:home/ramon/Mendeley-pdfs/waviCGH a web application for the analysis and visualization of genomic copy number alterations./2010/Nucleic acids research/waviCGH a web application for the analysis and visualization of genomic copy number alterations. - 2010 - Nucleic acids research.pdf:pdf},
issn = {1362-4962},
journal = {Nucleic acids research},
month = jul,
pages = {W182--7},
pmid = {20507915},
title = {{waviCGH: a web application for the analysis and visualization of genomic copy number alterations.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2896163\&tool=pmcentrez\&rendertype=abstract},
volume = {38 Suppl},
year = {2010}
}


@article{waves-hsu,
abstract = {Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number simultaneously at thousands of genomic loci. Typically, these measurements are reported and displayed linearly on chromosome maps, and gains and losses are detected as deviations from normal diploid cells. We propose that one may consider denoising the data to uncover the true copy number changes before drawing inferences on the patterns of aberrations in the samples. Nonparametric techniques are particularly suitable for data denoising as they do not impose a parametric model in finding structures in the data. In this paper, we employ wavelets to denoise the data as wavelets have sound theoretical properties and a fast computational algorithm, and are particularly well suited for handling the abrupt changes seen in array-CGH data. A simulation study shows that denoising data prior to testing can achieve greater power in detecting the aberrant spot than using the raw data without denoising. Finally, we illustrate the method on two array-CGH data sets.},
address = {Biostatistics Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue N, M2-B500, Seattle, WA 98109, USA. lih@fhcrc.org},
author = {Hsu, L and Self, S G and Grove, D and Randolph, T and Wang, K and Delrow, J J and Loo, L and Porter, P},
file = {:home/ramon/Mendeley-pdfs/Denoising array-based comparative genomic hybridization data using wavelets/Denoising array-based comparative genomic hybridization data using wavelets. - 2005 - Biostatistics.pdf:pdf},
issn = {1465-4644},
journal = {Biostatistics},
month = apr,
number = {2},
pages = {211--226},
title = {{Denoising array-based comparative genomic hybridization data using wavelets.}},
url = {http://view.ncbi.nlm.nih.gov/pubmed/15772101},
volume = {6},
year = {2005}
}




@article{biohmm,
abstract = {SUMMARY: We have developed a new method (BioHMM) for segmenting array comparative genomic hybridization data into states with the same underlying copy number. By utilizing a heterogeneous hidden Markov model, BioHMM incorporates relevant biological factors (e.g. the distance between adjacent clones) in the segmentation process.},
address = {Hutchison-MRC Research Centre, Department of Oncology, Computational Biology Group, University of Cambridge Hills Road, Cambridge. J.Marioni@damtp.cam.ac.uk},
author = {Marioni, J C and Thorne, N P and Tavar\'{e}, S},
file = {:home/ramon/Mendeley-pdfs/BioHMM a heterogeneous hidden Markov model for segmenting array CGH data/BioHMM a heterogeneous hidden Markov model for segmenting array CGH data. - 2006 - Bioinformatics.pdf:pdf},
issn = {1367-4803},
journal = {Bioinformatics},
month = may,
number = {9},
pages = {1144--1146},
title = {{BioHMM: a heterogeneous hidden Markov model for segmenting array CGH data.}},
url = {http://view.ncbi.nlm.nih.gov/pubmed/16533818},
volume = {22},
year = {2006}
}


@article{haarseg-paper,
abstract = {MOTIVATION: Array Comparative Genomic Hybridization (aCGH) is used to scan the entire genome for variations in DNA copy number. A central task in the analysis of aCGH data is the segmentation into groups of probes sharing the same DNA copy number. Some well known segmentation methods suffer from very long running times, preventing interactive data analysis. RESULTS: We suggest a new segmentation method based on wavelet decomposition and thresholding, which detects significant breakpoints in the data. Our algorithm is over 1000 times faster than leading approaches, with similar performance. Another key advantage of the proposed method is its simplicity and flexibility. Due to its intuitive structure, it can be easily generalized to incorporate several types of side information. Here, we consider two extensions which include side information indicating the reliability of each measurement, and compensating for a changing variability in the measurement noise. The resulting algorithm outperforms existing methods, both in terms of speed and performance, when applied on real high density CGH data. AVAILABILITY: Implementation is available under software tab at: http://www.ee.technion.ac.il/Sites/People/YoninaEldar/.},
address = {Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa Israel.},
author = {Ben-Yaacov, E and Eldar, Y C},
doi = {10.1093/bioinformatics/btn272},
file = {:home/ramon/Mendeley-pdfs/A fast and flexible method for the segmentation of aCGH data/A fast and flexible method for the segmentation of aCGH data. - 2008 - Bioinformatics (Oxford, England).pdf:pdf},
issn = {1460-2059},
journal = {Bioinformatics (Oxford, England)},
month = aug,
number = {16},
pages = {i139--i145},
title = {{A fast and flexible method for the segmentation of aCGH data.}},
url = {http://dx.doi.org/10.1093/bioinformatics/btn272},
volume = {24},
year = {2008}
}



@article{cghseg,
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.},
address = {Institut National Agronomique Paris-Grignon, UMR INAPG/ENGREF/INRA MIA 518, Paris, France. picard@inapg.fr},
author = {Picard, F and Robin, S and Lavielle, M and Vaisse, C and Daudin, J J},
doi = {10.1186/1471-2105-6-27},
file = {:home/ramon/Mendeley-pdfs/A statistical approach for array CGH data analysis/A statistical approach for array CGH data analysis. - 2005 - BMC Bioinformatics.pdf:pdf},
issn = {1471-2105},
journal = {BMC Bioinformatics},
title = {{A statistical approach for array CGH data analysis.}},
url = {http://dx.doi.org/10.1186/1471-2105-6-27},
volume = {6},
year = {2005}
}

@Article{glad-paper,
author = {Hupe, P.  and Stransky, N.  and Thiery, J. P.  and Radvanyi, F.  and
Barillot, E.},
title = {Analysis of array CGH data: from signal ratio to
gain and loss of DNA regions.},
journal = {Bioinformatics},
year = {2004},
pages = {3413--3422},
}


@Article{tilingarray,
    author = {Wolfgang Huber and Joern Toedling and Lars M. Steinmetz},
    title = {Transcript mapping with high-density oligonucleotide tiling arrays},
    journal = {Bioinformatics},
    year = {2006},
    volume = {22},
    pages = {1963-1970},
  }


@article{CBS-paper,
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.},
author = {Venkatraman, E S and Olshen, Adam B},
doi = {10.1093/bioinformatics/btl646},
file = {:home/ramon/Mendeley-pdfs/A faster circular binary segmentation algorithm for the analysis of array CGH data/A faster circular binary segmentation algorithm for the analysis of array CGH data. - 2007 - Bioinformatics (Oxford, England).pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
month = mar,
number = {6},
pages = {657--663},
title = {{A faster circular binary segmentation algorithm for the analysis of array CGH data.}},
url = {http://dx.doi.org/10.1093/bioinformatics/btl646},
volume = {23},
year = {2007}
}

  @Manual{glad-package,
    title = {GLAD: Gain and Loss Analysis of DNA},
    author = {Philippe Hupe},
    year = {2011},
    note = {R package version 2.27.0},
    url = {http://bioinfo.curie.fr},
  }

