  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.
