A flexible rank-based framework for detecting copy number aberrations from array data.
BACKGROUND
DNA copy number aberration--both inherited and sporadic--is a significant contributor to a variety of human diseases. Copy number characterization is therefore an area of intense research. Probe hybridization-based arrays are important tools used to measure copy number in a high-throughput manner.


RESULTS
In this article, we present a simple but powerful nonparametric rank-based approach to detect deletions and gains from raw array copy number measurements. We use three different rank-based statistics to detect three separate molecular phenomena-somatic lesions, germline deletions and germline gains. The approach is robust and rigorously grounded in statistical theory, thereby enabling the meaningful assignment of statistical significance to each putative aberration. We demonstrate the flexibility of our approach by applying it to data from three different array platforms. We show that our method compares favorably with established approaches by applying it to published well-characterized samples. Power simulations demonstrate exquisite sensitivity for array data of reasonable quality.


CONCLUSIONS
Our flexible rank-based framework is suitable for multiple platforms including single nucleotide polymorphism arrays and array comparative genomic hybridization, and can reliably detect gains or losses of genomic DNA, whether inherited, de novo, or somatic.


BACKGROUND
An R package RankCopy containing the methods described here, and is freely available from the author's web site (http://mendel.gene.cwru.edu/laframboiselab/).


BACKGROUND
Supplementary data are available at Bioinformatics online.
LaFramboise
Thomas
T
Department of Genetics, Case Western Reserve University, Cleveland, OH 44106, USA. Thomas.LaFramboise@case.edu
Winckler
Wendy
W
Thomas
Roman K
RK
D001321:Autistic Disorder / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry*; D018628:Gene Dosage; D014644:Genetic Variation; D016678:Genome; D006801:Humans; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis
D004247:DNA