Dataset Information
A genomic random interval model for statistical analysis of genomic lesion data.
Ontology highlight
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.
SUBMITTER: Pounds S
PROVIDER: S-EPMC3740633 | biostudies-literature | 2013 Sep
REPOSITORIES: biostudies-literature
Publications
A genomic random interval model for statistical analysis of genomic lesion data.
Bioinformatics (Oxford, England) 20130710 17
<h4>Motivation</h4>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.<h4>Results</h4>We introduce the gen ...[more]