期刊
GENOME BIOLOGY
卷 12, 期 4, 页码 -出版社
BMC
DOI: 10.1186/gb-2011-12-4-r41
关键词
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资金
- Genome Characterization Center [U24CA143867]
- National Institute of General Medical Sciences [T32GM07753]
- NIH [K08CA122833]
- V Foundation Scholarship
- Doris Duke Charitable Foundation
- NATIONAL CANCER INSTITUTE [U24CA143867, K08CA122833] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [T32GM007753] Funding Source: NIH RePORTER
We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.
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