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Issue No.05 - Sept.-Oct. (2012 vol.9)
pp: 1539-1545
M. N. Nounou , Chem. Eng. Program, Texas A&M Univ. at Qatar, Doha, Qatar
H. N. Nounou , Electr. & Comput. Eng. Program, Texas A&M Univ. at Qatar, Doha, Qatar
N. Meskin , Dept. Electr. Eng., Qatar Univ., Doha, Qatar
A. Datta , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
E. R. Dougherty , Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
ABSTRACT
Measured microarray genomic and metabolic data are a rich source of information about the biological systems they represent. For example, time-series biological data can be used to construct dynamic genetic regulatory network models, which can be used to design intervention strategies to cure or manage major diseases. Also, copy number data can be used to determine the locations and extent of aberrations in chromosome sequences. Unfortunately, measured biological data are usually contaminated with errors that mask the important features in the data. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, different batch as well as online multiscale filtering techniques are used to denoise biological data contaminated with white or colored noise. The performances of these techniques are demonstrated and compared to those of some conventional low-pass filters using two case studies. The first case study uses simulated dynamic metabolic data, while the second case study uses real copy number data. Simulation results show that significant improvement can be achieved using multiscale filtering over conventional filtering techniques.
INDEX TERMS
wavelet transforms, biology computing, cellular biophysics, diseases, filtering theory, genetics, genomics, lab-on-a-chip, low-pass filters, signal denoising, time series, simulated dynamic metabolic data, multiscale denoising, microarray genomic data, biological systems, time-series biological data, dynamic genetic regulatory network models, diseases, chromosome sequences, wavelet-based multiscale filtering, online multiscale filtering techniques, colored noise, white noise, conventional low-pass filters, Filtering, Biological system modeling, Noise measurement, DNA, Bioinformatics, Data models, copy number data., Wavelets, multiscale filtering, metabolic data
CITATION
M. N. Nounou, H. N. Nounou, N. Meskin, A. Datta, E. R. Dougherty, "Multiscale Denoising of Biological Data: A Comparative Analysis", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 5, pp. 1539-1545, Sept.-Oct. 2012, doi:10.1109/TCBB.2012.67
REFERENCES
[1] H. Jong, "Modeling and Simulation of Genetic Regulatory Systems: A Literature Review," J. Computational Biology, vol. 9, no. 1, pp. 67-103, 2002.
[2] I.-C. Chou, H. Martens, and E.O. Voit, "Parameter Estimation in Biochemical Systems Models with Alternating Regression," Theoretical Biology and Medical Modelling, vol. 3, no. 25, 2006.
[3] O.R.G. et al., "Parameter Estimation Using Simulated Annealing for S-System Models of Biochemical Networks," Bioinformatics, vol. 23, no. 4, pp. 480-486, 2007.
[4] Z. Kutalik, W. Tucker, and V. Moulton, "S-System Parameter Estimation for Noisy Metabolic Profiles Using Newton-Flow Analysis," IET System Biology, vol. 1, no. 3, pp. 174-180, 2007.
[5] H. Wang, L. Qian, and E. Dougherty, "Inference of Gene Regulatory Networks Using S-Systems: A Unified Approach," IET System Biology, vol. 4, no. 2, pp. 145-156, 2010.
[6] N. Meskin, H. Nounou, M. Nounou, A. Datta, and E.R. Dougherty, "Parameter Estimation of Biological Phenomena Modeled by S-Systems: An Extended Kalman Filter Approach," Proc. IEEE Conf. Decision and Control and European Control Conf., pp. 4424-4429, 2011.
[7] A. Ervadi-Radhakrishnan and E.O. Voit, "Controllabilty of Non-Linear Biochemical Systems," Math. Biosciences, vol. 196, pp. 99-123, 2005.
[8] N. Meskin, H. Nounou, M. Nounou, A. Dattta, and E.R. Dougherty, "Intervention in Biological Phenomena Modeled by S-Systems," IEEE Trans. Biomedical Eng., vol. 58, no. 1, pp. 1260-1267, 2011.
[9] A. Alqallaf and A. Tewfik, "DNA Copy Number Detection and Sigma Filter," Proc. IEEE Int'l Workshop Genomic Signal Processing and Statistics, pp. 1-4, 2007.
[10] H.W. Sorenson, Kalman Filtering: Theory and Applications. IEEE Press, 1985.
[11] J.B. Rawlings and B.R. Bakshi, "Particle Filtering and Moving Horizon Estimation," Computers and Chemical Eng. J., vol. 30, nos. 10-12, pp. 1529-1541, 2006.
[12] M.A. Kramer and R.S.H. Mah, "Model Based Monitoring," Proc. Int'l Conf. Foundations of Computer Aided Process Operations, 1994.
[13] M.T. Tham and A. Parr, "Succeed at On-Line Validation and Reconstruction of Data," Chemical Eng. Program, vol. 90, no. 5, p. 46, 1994.
[14] A. Arneodo, Y. d'Aubenton Carafa, B. Audit, E. Bacry, J. Muzy, and C. Thermes, "Wavelet Based Fractal Analysis of DNA Sequences," Physica D, vol. 1328, pp. 1-30, 1996.
[15] A. Arneodo, Y. d'Aubenton Carafa, E. Bacry, P. Graves, J. Muzy, and C. Thermes, "What Can We Learn with Wavelet about DNA Sequences," Physica A, vol. 249, pp. 439-448, 1998.
[16] G. Dodin, P. Vandergheynst, P. Levoir, C. Cordier, and L. Marcourt, "Fourier and Wavelet Transform Analysis, a Tool for Visualizing Regular Patterns in DNA Sequences," J. Theoretical Biology, vol. 206, pp. 323-326, 2000.
[17] P. Li, "Wavelets in Bioinformatics and Computational Biology: State of Art and Perspectives," Bioinformatics, vol. 19, pp. 2-9, 2003.
[18] N.N.O.S. Huang, H., and A. Vo, "Array CGH Data Modeling and Smoothing in Stationary Wavelet Packet Transform Domain," BMC Genomics, vol. 9, pp. S2-S17, 2008.
[19] N. Nguyen, "Stationary Wavelet Packet Transform and Dependent Laplacian Bivariate Shrinkage Estimator for Array-CGH Data Smoothing," J. Compuational Biology, vol. 17, no. 2, pp. 139-152, 2010.
[20] P. Morozov, T. Sitnikova, G. Churchill, F. Ayala, and A. Rzhetsky, "A New Method for Characterizing Replacement Rate Variation in Molecular Sequences," Genetics, vol. 154, pp. 381-395, 2000.
[21] A.R. Rzhetsky and P. Morozov, "Markov Chain Monte Carlo Computation of Confidence Intervals for Substitution-Rate Variation in Proteins," Proc. Pacific Symp. Biocomputing, pp. 203-214, 2001.
[22] P. Lio and M. Vannucci, "Wavelet Change-Point Prediction of Transmembrane Proteins," Bioinformatics, vol. 16, pp. 376-382, 2000.
[23] K. Murray, D. Gorse, and J. Thornton, "Wavelet Transforms for the Characterization and Detection of Repeating Motifs," J. Molecular Biology, vol. 316, pp. 341-363, 2002.
[24] A. Mandell, M. Owens, K. Selz, W. Morgan, M. Shesinger, and C. Nemeroff, "Mode Matches in Hydrophobic Free Energy Eigenfunctions Predicts Peptide-Protein Interactions," Biopolymers, vol. 46, pp. 89-101, 1998.
[25] H. Hirakawa, S. Muta, and S. Kuhara, "The Hydrophobic Cores of Proteins Predicted by Wavelet Analysis," Bioinformatics, vol. 15, pp. 141-148, 1999.
[26] P. Main and J. Wilson, "Low-Resolution Phase Extension Using Wavelet Analysis," Acta Crystallographica Section D Biological Crystallography, vol. 56, pp. 1324-1331, 2000.
[27] J. Ferrer, M. Roth, and A. Antoniadis, "Data Compression for Diffraction Patterns," Acta Crystallographica Section D Crystallography, vol. 54, pp. 184-199, 1998.
[28] M. Carson, "Wavelets and Molecular Structure," J. Computer-Aided Molecular Design, vol. 10, pp. 273-283, 1996.
[29] R. Klevecz, "Dynamic Architecture of the Yeast Cell Cycle Uncovered by Wavelet Decomposition," Functional and Integrative Genomics, vol. 1, pp. 186-192, 2000.
[30] R. Jornsten and B. Yu, "Comprestimation: Microarray Images in Abundance," Proc. Conf. Information Sciences and Systems, pp. 186-192, Mar. 2000.
[31] E. Myasnikova, A. Samsonova, K. Kozlov, M. Samsonova, and J. Reinitz, "Registration of the Expression Patterns of Drosophila Segmentation Genes by Two Independent Methods," Bioinformatics, vol. 17, pp. 3-12, 2001.
[32] I. Dinov, J. Boscardin, M. Mega, E. Sowell, and A. Toga, "A Wavelet-Based Statistical Analysis of Fmri Data: I. Motivation and Data Distribution Modeling," NeuroInformatics, vol. 3, no. 4, pp. 319-343, 2005.
[33] D. Nain, S. Haker, A. Bobick, and A. Tannenbaum, "Multiscale 3D Shape Representation and Segmentation Using Spherical Wavelets," IEEE Trans. Medical Imaging, vol. 26, no. 4, pp. 598-618, Apr. 2007.
[34] V. Prasad, P. Siddaiah, and B.P. Rao, "A New Wavelet Based Method for Denoising of Biological Signals," Int'l J. CS and Network Security, vol. 8, no. 1, pp. 238-244, 2008.
[35] R.D. Strum and D.E. Kirk, First Principles of Discrete Systems and Digital Signal Processing. Addison-Wesley, 1989.
[36] M. Nounou and B. Bakshi, "Online Multiscale Filtering of Random and Gross Errors without Process Models," AIChE J., vol. 45, no. 5, pp. 1041-1058, 1999.
[37] G. Strang, "Wavelets and Dilation Equations," SIAM Rev., vol. 31, pp. 614-627, 1989.
[38] I. Daubechies, "Orthonormal Bases for Compactly Supported Wavelets," Comm. Pure and Applied Math., vol. 41, pp. 909-996, 1988.
[39] S. Mallat, "A Theory of Multiresolution Signal Decomposition: The Wavelet Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, July 1989.
[40] A. Cohen, I. Daubechies, and V. Pierre, "Wavelets on the Interval and Fast Wavelet Transforms," Applied and Computational Harmonic Analysis, vol. 1, no. 1, pp. 54-81, 1993.
[41] D. Donoho and I. Johnstone, "Ideal De-Noising in an Orthonormal Basis Chosen from a Library of Bases," technical report, Dept. of Statistics, Stanford Univ., 1994.
[42] D. Donoho, I. Johnstone, G. Kerkyacharian, and D. Picard, "Wavelet Shrinkage: Asymptotia?" J. Royal Statistics Soc. B, vol. 57, no. 2, pp. 301-369, 1995.
[43] B. Bakshi, "Multiscale Analysis and Modeling Using Wavelets," Chemometrics, vol. 13, nos. 3/4, pp. 415-434, 1999.
[44] M. Nounou and B. Bakshi, "Multiscale Methods for Denoising and Compression," Wavelets in Analytical Chemistry, B. Walczak ed., pp. 119-150, Elsevier, 2000.
[45] D. Donoho and I. Johnstone, "Ideal Spatial Adaptation Via Wavelet Shrinkage," Biometrika, vol. 81, pp. 425-455, 1994.
[46] G. Nason, "Wavelet Shrinkage Using Cross-Validation," J. Royal Statistical Soc. B, vol. 58, p. 463, 1996.
[47] R. Coifman and D. Donoho, "Translation-Invariant De-Noising," Lecture Notes in Statistics, vol. 103, pp. 125-150, 1995.
[48] E.O. Voit and J. Almeida, "Decoupling Dynamical Systems for Pathway Identification from Metabolic Profiles," Bioinformatics, vol. 20, no. 11, pp. 1670-1681, 2004.
[49] A. Snijders, N. Nowak, R. Segraves, S. Blackwood, N. Brown, J. Conroy, G. Hamilton, A. Hindle, B. Huey, K. Kimura, S. Law, K. Myambo, J. Palmer, B. Ylstra, J. Yue, J. Gray, A. Jain, D. Pinkel, and D. Albertson, "Assembly of Microarrays for Genome-Wide Measurement of DNA Copy Number," Nature Genetics, vol. 29, pp. 263-264, 2001.
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