Optimal Sparse Segment Identification with Application in Copy Number Variation Analysis
X. Jessie Jeng, Tony Cai and Hongzhe Li
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Abstract:
Motivated by DNA copy number analysis in genetics,
we consider the problem of detecting and identifying sparse short
segments in a long one-dimensional sequence of data with additive
Gaussian white noise, where the number, length and
location of the segments are unknown. We present a statistical
characterization of the identifiable region of a segment where it is
possible to reliably separate the segment from Gaussian noise.
An efficient likelihood ratio selection (LRS)
procedure for identifying the segments is developed and the asymptotic
optimality of this method is presented in the sense that the LRS can
separate the signal segments from the noise as long as the signal segments can
be estimated. The proposed method is demonstrated with simulations
and analysis of a real data set on identification of copy number
variants based on high-density single nucleotide polymorphisms (SNP)
data. The results show that the LRS procedure can
yield greater gain in power of detecting the true segments than some
standard signal identification methods.
- Paper:
pdf file.
- Other related papers:
Cai, T. T., Jeng, J. & Li, H. (2012).
Robust detection and identification of sparse segments in ultra-high dimensional data analysis.
Journal of the Royal Statistical Society, Series B 74, 773-797.Jeng, J., Cai, T. T. & Li, H. (2013).
Simultaneous discovery of rare and common segment variants.
Biometrika 100, 157-172.