@article{CGHcall,
   author	= "van de Wiel, Mark A. and Kim, Kyung In and Vosse, Sjoerd J. and van Wieringen, Wessel N. and Wilting, Saskia M. and Ylstra, Bauke",
   title	= "{CGHcall:} Calling Aberrations for Array {CGH} Tumor Profiles",
   journal	= "Bioinformatics",
   year		= "2007",
   volume	= "23",
   pages	= "892-894",
   pmid		= "17267432",
   abstract	= "CGHcall achieves high calling accuracy for array CGH data by effective use of breakpoint information from segmentation and by inclusion of several biological concepts that are ignored by existing algorithms. The algorithm is validated for simulated and verified real array CGH data. By incorporating more than three classes, CGHcall improves detection of single copy gains and amplifications. Moreover, it allows effective inclusion of chromosome arm information. AVAILABILITY: An R-package (GUI), a manual and an example data set are available at http://www.few.vu.nl/~mavdwiel/CGHcall.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online."
}

@article{DNAcopy,
   author	= "Venkatraman, E. S. and Olshen, Adam B.",
   title	= "{A} Faster Circular Binary Segmentation Algorithm for the Analysis of Array {CGH} data",
   journal	= "Bioinformatics",
   year		= "2007",
   volume	= "23",
   pages	= "657-663",
   pmid		= "17234643",
   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."
}

@article{Wilting,
   author	= "Wilting, S. M. and Snijders, P. J. F. and Meijer, G. A. and Ylstra, B. and van den Ijssel, P. R. L. A. and Snijders, A. M. and Albertson, D. G. and Coffa, J. and Schouten, J. P. and van de Wiel, M. A. and Meijer, C. J. L. M. and Steenbergen, R. D. M.",
   title	= "{Increased} gene copy Numbers at Chromosome 20q are Frequent in both Squamous cell Carcinomas and Adenocarcinomas of the Cervix",
   journal	= "J Pathol",
   year		= "2006",
   volume	= "209",
   pages	= "220-230",
   pmid		= "16538612",
   abstract	= "Genome-wide microarray-based comparative genomic hybridization (array CGH) was used to identify common chromosomal alterations involved in cervical carcinogenesis as a first step towards the discovery of novel biomarkers. The genomic profiles of nine squamous cell carcinomas (SCCs) and seven adenocarcinomas (AdCAs), as well as four human papillomavirus (HPV)-immortalized keratinocyte cell lines, were assessed. On a genome-wide scale, SCCs showed significantly more gains than AdCAs. More specifically, there was a striking and highly significant difference between the two histological types for gain at 3q12.1-28, which was predominantly observed in SCC. Other frequent alterations included gains of 1q21.1-31.1 and 20q11.21-13.33, and losses of 11q22.3-25 and 13q14.3-21.33. Subsequent FISH analysis for hTR, located at 3q26, confirmed the presence of 3q gain in SCCs and HPV-immortalized cell lines. Fine mapping of chromosome 20q using multiplex ligation-dependent probe amplification (MLPA) showed copy number increases for a number of genes located at 20q11-q12, including DNMT3B and TOP1. For DNMT3B, this correlated with elevated mRNA expression in 79\% of cases. In conclusion, the assessment of frequent genomic alterations resulted in the identification of potential novel biomarkers, which may ultimately enable a better risk stratification of high-risk (hr)-HPV-positive women."
}

@article{Impute,
   author	= "Troyanskaya, O. and Cantor, M. and Sherlock, G. and Brown, P. and Hastie, T. and Tibshirani, R. and Botstein, D. and Altman, R. B.",
   title	= "{Missing} Value Estimation Methods for {DNA} Microarrays",
   journal	= "Bioinformatics",
   year		= "2001",
   volume	= "17",
   pages	= "520-525",
   pmid		= "11395428",
   abstract	= "MOTIVATION: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. RESULTS: We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20\% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions."
}
