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Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain

BACKGROUND: Array-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy n...

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Autores principales: Huang, Heng, Nguyen, Nha, Oraintara, Soontorn, Vo, An
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559881/
https://www.ncbi.nlm.nih.gov/pubmed/18831782
http://dx.doi.org/10.1186/1471-2164-9-S2-S17
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author Huang, Heng
Nguyen, Nha
Oraintara, Soontorn
Vo, An
author_facet Huang, Heng
Nguyen, Nha
Oraintara, Soontorn
Vo, An
author_sort Huang, Heng
collection PubMed
description BACKGROUND: Array-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy number changes is important for both the basic understanding of cancer and its diagnosis. In order to develop effective methods to identify aberration regions from array CGH data, many recent research work focus on both smoothing-based and segmentation-based data processing. In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH data. Our purpose is to remove CGH noise in whole frequency while keeping true signal by using bivariate model. RESULTS: In both synthetic and real CGH data, Stationary Wavelet Packet Transform (SWPT) is the best wavelet transform to analyze CGH signal in whole frequency. We also introduce a new bivariate shrinkage model which shows the relationship of CGH noisy coefficients of two scales in SWPT. Before smoothing, the symmetric extension is considered as a preprocessing step to save information at the border. CONCLUSION: We have designed the SWTP and the SWPT-Bi which are using the stationary wavelet packet transform with the hard thresholding and the new bivariate shrinkage estimator respectively to smooth the array CGH data. We demonstrate the effectiveness of our approach through theoretical and experimental exploration of a set of array CGH data, including both synthetic data and real data. The comparison results show that our method outperforms the previous approaches.
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spelling pubmed-25598812008-10-04 Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain Huang, Heng Nguyen, Nha Oraintara, Soontorn Vo, An BMC Genomics Research BACKGROUND: Array-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy number changes is important for both the basic understanding of cancer and its diagnosis. In order to develop effective methods to identify aberration regions from array CGH data, many recent research work focus on both smoothing-based and segmentation-based data processing. In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH data. Our purpose is to remove CGH noise in whole frequency while keeping true signal by using bivariate model. RESULTS: In both synthetic and real CGH data, Stationary Wavelet Packet Transform (SWPT) is the best wavelet transform to analyze CGH signal in whole frequency. We also introduce a new bivariate shrinkage model which shows the relationship of CGH noisy coefficients of two scales in SWPT. Before smoothing, the symmetric extension is considered as a preprocessing step to save information at the border. CONCLUSION: We have designed the SWTP and the SWPT-Bi which are using the stationary wavelet packet transform with the hard thresholding and the new bivariate shrinkage estimator respectively to smooth the array CGH data. We demonstrate the effectiveness of our approach through theoretical and experimental exploration of a set of array CGH data, including both synthetic data and real data. The comparison results show that our method outperforms the previous approaches. BioMed Central 2008-09-16 /pmc/articles/PMC2559881/ /pubmed/18831782 http://dx.doi.org/10.1186/1471-2164-9-S2-S17 Text en Copyright © 2008 Huang et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Huang, Heng
Nguyen, Nha
Oraintara, Soontorn
Vo, An
Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain
title Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain
title_full Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain
title_fullStr Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain
title_full_unstemmed Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain
title_short Array CGH data modeling and smoothing in Stationary Wavelet Packet Transform domain
title_sort array cgh data modeling and smoothing in stationary wavelet packet transform domain
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2559881/
https://www.ncbi.nlm.nih.gov/pubmed/18831782
http://dx.doi.org/10.1186/1471-2164-9-S2-S17
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