GIMscan: a new statistical method for analyzing whole-genome array CGH data

Authors:
Yanxin Shi;Fan Guo;Wei Wu;Eric P. Xing
Affiliations:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Venue:
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Year:
2007

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Abstract

Genetic instability represents an important type of biological markers for cancer and many other diseases. Array Comparative Genome Hybridization (aCGH) is a high-throughput cytogenetic technique that can efficiently detect genome-wide genetic instability events such as chromosomal gain, loss, and more complex aneuploidity, collectively known as genome imbalance (GIM). We propose a new statistical method, Genome Imbalance Scanner (GIMscan), for automatically decoding the underlying DNA dosage states from aCGH data. GIMscan captures both the intrinsic (nonrandom) spatial change of genome hybridization intensities, and the prevalent (random) measurement noise during data acquisition; and it simultaneously segments the chromosome and assigns different states to the segmented DNA. We tested the proposed method on both simulated data and real data measured from a colorectal cancer population, and we report competitive or superior performance of GIMscan in comparison with popular extant methods.