The screening and ranking algorithm to detect DNA copy number variations

Abstract

DNA Copy number variation (CNV) has recently gained considerable interest as a source of genetic variation that likely influences phenotypic differences. Many statistical and computational methods have been proposed and applied to detect CNVs based on data that generated by genome analysis platforms. However, most algorithms are computationally intensive with complexity at least $O(n^2)$, where $n$ is the number of probes in the experiments. Moreover, the theoretical properties of those existing methods are not well understood. A faster and better characterized algorithm is desirable for the ultra high throughput data. In this study, we propose the Screening and Ranking algorithm (SaRa) which can detect CNVs fast and accurately with complexity down to O(n). In addition, we characterize theoretical properties and present numerical analysis for our algorithm.

Published in at http://dx.doi.org/10.1214/12-AOAS539 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (3)

• Pathwise coordinate optimization
• Properties and refinements of the fused lasso
• The screening and ranking algorithm to detect DNA copy number variations

Cited by in corpus (1)

• The screening and ranking algorithm to detect DNA copy number variations