Functional performance of aCGH design for clinical cytogenetics
Computers in Biology and Medicine
Multisample aCGH Data Analysis via Total Variation and Spectral Regularization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Wavelet Analysis in Current Cancer Genome Research: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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/ Contact: yonina@ee.technion.ac.il