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

Bioinformatics 2013 Sep 10;29(17):2088-95. Epub 2013 Jul 10.

Department of Biostatistics, Department of Computational Biology and Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN 38135, USA.

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.

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Source
https://academic.oup.com/bioinformatics/article-lookup/doi/1
Publisher Site
http://dx.doi.org/10.1093/bioinformatics/btt372DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3740633PMC
September 2013
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