CGHTRIMMER: Discretizing noisy Array CGH Data
msra(2010)
摘要
The development of cancer is largely driven by the gain or loss of subsets of
the genome, promoting uncontrolled growth or disabling defenses against it.
Identifying genomic regions whose DNA copy number deviates from the normal is
therefore central to understanding cancer evolution. Array-based comparative
genomic hybridization (aCGH) is a high-throughput technique for identifying DNA
gain or loss by quantifying total amounts of DNA matching defined probes
relative to healthy diploid control samples. Due to the high level of noise in
microarray data, however, interpretation of aCGH output is a difficult and
error-prone task.
In this work, we tackle the computational task of inferring the DNA copy
number per genomic position from noisy aCGH data. We propose CGHTRIMMER, a
novel segmentation method that uses a fast dynamic programming algorithm to
solve for a least-squares objective function for copy number assignment.
CGHTRIMMER consistently achieves superior precision and recall to leading
competitors on benchmarks of synthetic data and real data from the Coriell cell
lines. In addition, it finds several novel markers not recorded in the
benchmarks but plausibly supported in the oncology literature. Furthermore,
CGHTRIMMER achieves superior results with run-times from 1 to 3 orders of
magnitude faster than its state-of-art competitors.
CGHTRIMMER provides a new alternative for the problem of aCGH discretization
that provides superior detection of fine-scale regions of gain or loss yet is
fast enough to process very large data sets in seconds. It thus meets an
important need for methods capable of handling the vast amounts of data being
accumulated in high-throughput studies of tumor genetics.
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关键词
cell line,genetics,copy number,objective function,microarray data,high throughput,quantitative method,dynamic programming algorithm,synthetic data,least square
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