Robust Detection and Identification of Sparse Segments in Ultra-High Dimensional Data Analysis
Tony Cai , X. Jessie Jeng, and Hongzhe Li
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Abstract:
Motivated by copy number variation analysis based on next generation sequencing
data, we consider the problem of detecting and identifying sparse short segments hidden in
an ultra long linear sequence of data with unspecified noise distribution. Based on a local
median transformation, we propose a computationally efficient method called robust segment
identifier (RSI), which provides a robust and optimal solution for segment identification over
a wide range of noise distributions. We theoretically quantify the conditions for detecting the
segment signals and show that the RSI consistently estimates the signal segments whenever
it is possible to detect their existence. We present simulations to demonstrate the effect of
data transformation and the efficiency of our method under different noise distributions. We
also present results from an application to copy number variant analysis using next generation
sequencing data of the HapMap Yoruban sample NA19240 to further illustrate the theory and
the methods.
- Paper:
pdf file.
- Other related papers:
Jeng, X. J., Cai, T., and Li, H. (2010).
Optimal sparse segment identification with application in copy number variation analysis.
Journal of American Statistical Association 105, 1156-1166.Jeng, J., Cai, T. T. & Li, H. (2013).
Simultaneous discovery of rare and common segment variants.
Biometrika 100, 157-172.