Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data.

@article{citeulike:326502, 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 data sets, the relative merits of these methods in practice are not clear. RESULTS: We compare eleven different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on such diverse techniques as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets, genetic algorithms, and others. 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 data set 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.}, address = {Harvard-Partners Center for Genetics and Genomics, 77 Avenue Louis Pasteur, Boston, MA, 02115.}, author = {Lai, Weil R R. and Johnson, Mark D D. and Kucherlapati, Raju and Park, Peter J J. }, citeulike-article-id = {326502}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {geneticalgorithms}, month = {August}, posted-at = {2007-10-04 20:06:41}, priority = {2}, title = {Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16081473}, year = {2005} }

TY - JOUR ID - citeulike:326502 L3 - citeulike-article-id:326502 TI - Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. JF - Bioinformatics AD - Harvard-Partners Center for Genetics and Genomics, 77 Avenue Louis Pasteur, Boston, MA, 02115. SN - 1367-4803 N2 - 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 data sets, the relative merits of these methods in practice are not clear. RESULTS: We compare eleven different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on such diverse techniques as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets, genetic algorithms, and others. 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 data set 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. KW - geneticalgorithms AU - Lai, Weil R AU - Johnson, Mark D AU - Kucherlapati, Raju AU - Park, Peter J PY - 2005/08/04/ UR - http://view.ncbi.nlm.nih.gov/pubmed/16081473 ER -