Analysis of array CGH data: from signal ratio to gain and loss of DNA regions

By P. Hupé, Nicolas Stransky, J. P. Thiery, F. Radvanyi, E. Barillot. Bioinform., 2004

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A statistical approach for array CGH data analysis (2005, methodology): “…have been proposed: the likelihood criterion [9,11], the least-squares criterion [7], partial sums [10], and algorithms of optimization are based on genetic algorithms [9], dynamic programing [7], binary segmentation (DNAcopy R-package [10]) and adaptive weigths smoothing (GLAD R-package [11]).”
Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data (2005, methodology): “In Hupe et al. (2004), a more complex likelihood function with weights determined adaptively is used to solve the estimation problem locally based on data smoothed by the Adaptive Weights Smoothing procedure (Polzehl and Spokoiny, 2000).”
GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers (2011, methodology): “The first step, accurately defining the copy-number profile of each cancer sample, has been addressed by multiple previous studies [28-35] and is not discussed in detail here.”
PDGF-C mediates the angiogenic and tumorigenic properties of fibroblasts associated with tumors refractory to anti-VEGF treatment. (2009, methodology): “The resulting Agilent expression ratios were log2 transformed, centered to a median of zero, and segmented using GLAD (Hupe et al., 2004).”