Integrating copy number polymorphisms into array CGH analysis using a robust HMM.

@article{citeulike:789789, abstract = {MOTIVATION: Array comparative genomic hybridization (aCGH) is a pervasive technique used to identify chromosomal aberrations in human diseases, including cancer. Aberrations are defined as regions of increased or decreased DNA copy number, relative to a normal sample. Accurately identifying the locations of these aberrations has many important medical applications. Unfortunately, the observed copy number changes are often corrupted by various sources of noise, making the boundaries hard to detect. One popular current technique uses hidden Markov models (HMMs) to divide the signal into regions of constant copy number called segments; a subsequent classification phase labels each segment as a gain, a loss or neutral. Unfortunately, standard HMMs are sensitive to outliers, causing over-segmentation, where segments erroneously span very short regions. RESULTS: We propose a simple modification that makes the HMM robust to such outliers. More importantly, this modification allows us to exploit prior knowledge about the likely location of "outliers", which are often due to copy number polymorphisms (CNPs). By "explaining away" these outliers with prior knowledge about the locations of CNPs, we can focus attention on the more clinically relevant aberrated regions. We show significant improvements over the current state of the art technique (DNAcopy with MergeLevels) on previously published data from mantle cell lymphoma cell lines, and on published benchmark synthetic data augmented with outliers. AVAILABILITY: Source code written in Matlab is available from http://www.cs.ubc.ca/\~{}sshah/acgh. Contact: sshah@cs.ubc.ca.}, address = {Department of Computer Science, University of British Columbia 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada.}, author = {Shah, S. P. and Xuan, X. and Deleeuw, R. J. and Khojasteh, M. and Lam, W. L. and Ng, R. and Murphy, K. P. }, citeulike-article-id = {789789}, comment = {See also additional links}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {acgh, hmm}, month = {July}, number = {14}, posted-at = {2006-08-09 13:15:18}, priority = {5}, title = {Integrating copy number polymorphisms into array CGH analysis using a robust HMM.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16873504}, volume = {22}, year = {2006} }

TY - JOUR ID - citeulike:789789 L3 - citeulike-article-id:789789 TI - Integrating copy number polymorphisms into array CGH analysis using a robust HMM. JF - Bioinformatics VL - 22 IS - 14 AD - Department of Computer Science, University of British Columbia 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada. SN - 1460-2059 N2 - MOTIVATION: Array comparative genomic hybridization (aCGH) is a pervasive technique used to identify chromosomal aberrations in human diseases, including cancer. Aberrations are defined as regions of increased or decreased DNA copy number, relative to a normal sample. Accurately identifying the locations of these aberrations has many important medical applications. Unfortunately, the observed copy number changes are often corrupted by various sources of noise, making the boundaries hard to detect. One popular current technique uses hidden Markov models (HMMs) to divide the signal into regions of constant copy number called segments; a subsequent classification phase labels each segment as a gain, a loss or neutral. Unfortunately, standard HMMs are sensitive to outliers, causing over-segmentation, where segments erroneously span very short regions. RESULTS: We propose a simple modification that makes the HMM robust to such outliers. More importantly, this modification allows us to exploit prior knowledge about the likely location of "outliers", which are often due to copy number polymorphisms (CNPs). By "explaining away" these outliers with prior knowledge about the locations of CNPs, we can focus attention on the more clinically relevant aberrated regions. We show significant improvements over the current state of the art technique (DNAcopy with MergeLevels) on previously published data from mantle cell lymphoma cell lines, and on published benchmark synthetic data augmented with outliers. AVAILABILITY: Source code written in Matlab is available from http://www.cs.ubc.ca/~sshah/acgh. Contact: sshah@cs.ubc.ca. KW - acgh KW - hmm N1 - See also additional links AU - Shah, SP AU - Xuan, X AU - Deleeuw, RJ AU - Khojasteh, M AU - Lam, WL AU - Ng, R AU - Murphy, KP PY - 2006/07/15/ UR - http://view.ncbi.nlm.nih.gov/pubmed/16873504 ER -