A statistical approach for array CGH data analysis

By Franck Picard, Stéphane Robin, Marc Lavielle, C. Vaisse, Jean-Jacques Daudin. BMC Bioinformatics, 2005

412 citations recorded by the source corpus.

BackgroundMicroarray-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.ResultsWe 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.ConclusionsArray 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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b68e4acd77942ae16247724575eaf9b4117af40d

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Citations in the corpus

A robust approach for estimating change-points in the mean of an AR(1) process (2014, background): “Change-point detection problems arise in many fields, such as genomics [8,9,31], medical imaging [20], earth sciences [15,36], econometrics [17,19] or climate [25,28].”
Selective review of offline change point detection methods (2020, background): “Applications of BinSeg range from financial time series [7, 63, 113, 126, 127] to context recognition for mobile devices [128] and array-based DNA copy number data [19, 125, 129].”
A review of change point detection methods (2018, methodology): “Applications of BinSeg range from financial time series [9, 32, 33, 52, 92] to context recognition for mobile devices [67] and array-based DNA copy number data [106, 112].”
A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data (2007, background): “Biometrics 63, 22–32 March 2007 DOI: 10.1111/j.1541-0420.2006.00662.x A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data Nancy R. Zhang∗ and David O. Siegmund∗∗ Department of Statistics, Stanford University, Stanford, California 94305, ”
Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data (2005, methodology): “Red is CGHseg (Picard et al., 2005), orange is quantreg (Eilers and de Menezes, 2005), dark yellow is CLAC (Wanget al.”
Optimal detection of changepoints with a linear computational cost (2011, background): “There is a growing body of research (see Guyon and Yao, 1999; Picard et al., 2005; Birge and Massart, 2007) that consider nonlinear penalty forms.”
Spatial smoothing and hot spot detection for CGH data using the fused lasso. (2008, methodology): “Biostatistics (2007), 0, 0, pp. 1–12 doi:10.1093/biostatistics/kxm013 Spatial smoothing and hot spot detection for CGH data using the fused lasso”
changepoint: An R Package for Changepoint Analysis (2014, background): “The multiple parameter changepoint problem has been considered by many authors including Horvath (1993) and Picard et al. (2005).”