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Heavy-Tailed Noise Suppression and Derivative Wavelet Scalogram for Detecting DNA Copy Number Aberrations
- Source :
- IEEE/ACM transactions on computational biology and bioinformatics. 15(5)
- Publication Year :
- 2017
-
Abstract
- Most existing array comparative genomic hybridization (array CGH) data processing methods and evaluation models assumed that the probability density function (pdf) of noise in array CGH data is a Gaussian distribution. However, in practice, such noise distribution is peaky and heavy-tailed. Therefore, a Gaussian pdf is not adequate to approximate the noise in array CGH data and hence introduces wrong detections of chromosomal aberrations and leads misunderstanding on disease pathogenesis. A more accurate and sufficient model of noise in array CGH data is necessary and beneficial to the detection of DNA copy number variations. We analyze the real array CGH data from different platforms and show that the distribution of noise in array CGH data is fitted very well by generalized Gaussian distribution (GGD). Based on our new noise model, we propose a novel array CGH processing method combining the advantages of both the smoothing and segmentation approaches. The new method uses generalized Gaussian bivariate shrinkage function and one-directional derivative wavelet scalogram in generalized Gaussian noise. In the smoothing step, with the new generalized Gaussian noise model, we derive the heavy-tailed noise suppression algorithm in stationary wavelet domain. In the segmentation step, the 1D Gaussian derivative wavelet scalogram is employed to detect break points. Both real and simulated array CGH data with different noises (such as Gaussian noise, GGD noise, and real noise) are used in our experiments. We demonstrate that our new method outperforms other state-of-the-art methods, in terms of both root mean squared errors and receiver operating characteristic curves.
- Subjects :
- 0301 basic medicine
DNA Copy Number Variations
Gaussian
0206 medical engineering
Wavelet Analysis
Probability density function
02 engineering and technology
Data modeling
03 medical and health sciences
symbols.namesake
Wavelet
Statistics
Genetics
Animals
Humans
Generalized normal distribution
Mathematics
Comparative Genomic Hybridization
Applied Mathematics
DNA
Genomics
Quantitative Biology::Genomics
Noise
030104 developmental biology
Gaussian noise
symbols
Macaca
Algorithm
020602 bioinformatics
Smoothing
Algorithms
Biotechnology
Subjects
Details
- ISSN :
- 15579964
- Volume :
- 15
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM transactions on computational biology and bioinformatics
- Accession number :
- edsair.doi.dedup.....929fa3ac0a7b0da82fa688782ca9d4d9