THE SCREENING AND RANKING ALGORITHM TO DETECT DNA COPY NUMBER VARIATIONS.
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 leastO(n2), wherenis 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 toO(n). In addition, we characterize theoretical properties and present numerical analysis for our algorithm.
Department of Mathematics University of Arizona Tucson, Arizona 85721 USA yueniu@math.arizona.edu.
Yue S
YS
Niu
Heping
H
Zhang
Change-point detection; copy number variations; high dimensional data; screening and ranking algorithm