The screening and ranking algorithm to detect DNA copy number variations

Yue Niu, Heping Zhang

Research output: Contribution to journalArticle

20 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1306-1326
Number of pages21
JournalAnnals of Applied Statistics
Volume6
Issue number3
DOIs
StatePublished - Sep 2012
Externally publishedYes

Fingerprint

Screening
Ranking
DNA
Genetic Variation
Computational methods
Computational Methods
Statistical method
High Throughput
Numerical analysis
Numerical Analysis
Statistical methods
Genome
Probe
Genes
Likely
Throughput
Experiment
Experiments

Keywords

  • Change-point detection
  • Copy number variations
  • High dimensional data
  • Screening and ranking algorithm

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability

Cite this

The screening and ranking algorithm to detect DNA copy number variations. / Niu, Yue; Zhang, Heping.

In: Annals of Applied Statistics, Vol. 6, No. 3, 09.2012, p. 1306-1326.

Research output: Contribution to journalArticle

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