Modeling recurrent DNA copy number alterations in array CGH data
1Department of Computer Science, University of British Columbia, 201-2366 Main Mall Vancouver, BC V6T 1Z4 Canada and 2BC Cancer Research Centre, 675 W 10th Ave Vancouver, BC V5Z 1L3, Canada
*To whom correspondence should be addressed.
 |
Abstract |
|---|
Motivation: Recurrent DNA copy number alterations (CNA) measured with array comparative genomic hybridization (aCGH) reveal important molecular features of human genetics and disease. Studying aCGH profiles from a phenotypic group of individuals can determine important recurrent CNA patterns that suggest a strong correlation to the phenotype. Computational approaches to detecting recurrent CNAs from a set of aCGH experiments have typically relied on discretizing the noisy log ratios and subsequently inferring patterns. We demonstrate that this can have the effect of filtering out important signals present in the raw data. In this article we develop statistical models that jointly infer CNA patterns and the discrete labels by borrowing statistical strength across samples.
Results: We propose extending single sample aCGH HMMs to the multiple sample case in order to infer shared CNAs. We model recurrent CNAs as a profile encoded by a master sequence of states that generates the samples. We show how to improve on two basic models by performing joint inference of the discrete labels and providing sparsity in the output. We demonstrate on synthetic ground truth data and real data from lung cancer cell lines how these two important features of our model improve results over baseline models. We include standard quantitative metrics and a qualitative assessment on which to base our conclusions.
Availability: http://www.cs.ubc.ca/~sshah/acgh
Contact: sshah{at}cs.ubc.ca
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  O. M. Rueda and R. Diaz-Uriarte RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions Bioinformatics, August 1, 2009; 25(15): 1959 - 1960. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Choi, A. I. Nesvizhskii, D. Ghosh, and Z. S. Qin Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data Bioinformatics, July 15, 2009; 25(14): 1715 - 1721. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Barutcuoglu, E. M. Airoldi, V. Dumeaux, R. E. Schapire, and O. G. Troyanskaya Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields Bioinformatics, May 15, 2009; 25(10): 1307 - 1313. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K.-J. J. Cheung, S. P. Shah, C. Steidl, N. Johnson, T. Relander, A. Telenius, B. Lai, K. P. Murphy, W. Lam, A. J. Al-Tourah, et al. Genome-wide profiling of follicular lymphoma by array comparative genomic hybridization reveals prognostically significant DNA copy number imbalances Blood, January 1, 2009; 113(1): 137 - 148. [Abstract] [Full Text] [PDF]
|
|