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A genomic random interval model for statistical analysis of genomic lesion data

2013; Oxford University Press; Volume: 29; Issue: 17 Linguagem: Inglês

10.1093/bioinformatics/btt372

1367-4811

Stanley Pounds, Cheng Cheng, Shaoyu Li, Zhifa Liu, Jinghui Zhang, Charles G. Mullighan,

Cancer Genomics and Diagnostics

Abstract 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. Availability: An R package will be freely available at CRAN and www.stjuderesearch.org/site/depts/biostats/software. Contact: stanley.pounds@stjude.org Supplementary information: Supplementary data are available at Bioinformatics online.