Hidden Markov models approach to the analysis of array CGH data

Authors:
Jane Fridlyand;Antoine M. Snijders;Dan Pinkel;Donna G. Albertson;Ajay N. Jain
Affiliations:
UCSF Comprehensive Cancer Center, 2340 Sutter Str. N412, San Francisco, CA;UCSF Comprehensive Cancer Center, 2340 Sutter Str. N412, San Francisco, CA;UCSF Comprehensive Cancer Center, 2340 Sutter Str. N412, San Francisco, CA;UCSF Comprehensive Cancer Center, 2340 Sutter Str. N412, San Francisco, CA;UCSF Comprehensive Cancer Center, 2340 Sutter Str. N412, San Francisco, CA
Venue:
Journal of Multivariate Analysis
Year:
2004

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Abstract

The development of solid tumors is associated with acquisition of complex genetic alterations, indicating that failures in the mechanisms that maintain the integrity of the genome contribute to tumor evolution. Thus, one expects that the particular types of genomic alterations seen in tumors reflect underlying failures in maintenance of genetic stability, as well as selection for changes that provide growth advantage. In order to investigate genomic alterations we are using microarray-based comparative genomic hybridization (array CGH). The computational task is to map and characterize the number and types of copy number alternations present in the tumors, and so define copy number phenotypes and associate them with known biological markers.To utilize the spatial coherence between nearby clones. we use an unsupervised hidden Markov models approach. The clones are partitioned into the states which represent the underlying copy number of the group of clones. The method is demonstrated on the two cell line datasets, one with known copy number alterations. The biological conclusions drawn from the analyses are discussed.