期刊
BIOINFORMATICS
卷 29, 期 17, 页码 2088-2095出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btt372
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资金
- NCI
- NIGMS
- NHLBI of the National Institutes of Health [CA21765, U01GM 92666, U19HL065962]
- American Lebanese Syrian Associated Charities (ALSAC)
- NATIONAL CANCER INSTITUTE [P30CA021765] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [U19HL065962] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [U01GM092666] Funding Source: NIH RePORTER
Motivation: Tumors exhibit numerous genomic lesions such as copy number variations, structural variations and sequence variations. It is difficult to determine whether a specific constellation of lesions observed across a cohort of multiple tumors provides statistically significant evidence that the lesions target a set of genes that may be located across different chromosomes but yet are all involved in a single specific biological process or function. Results: We introduce the genomic random interval (GRIN) statistical model and analysis method that evaluates the statistical significance of the abundance of genomic lesions that overlap a specific locus or a pre-defined set of biologically related loci. The GRIN model retains certain biologically important properties of genomic lesions that are ignored by other methods. In a simulation study and two example analyses of leukemia genomic lesion data, GRIN more effectively identified important loci as significant than did three methods based on a permutation-of-markers model. GRIN also identified biologically relevant pathways with a significant abundance of lesions in both examples.
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