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International Journal of Machine Learning and Cybernetics

01-03-2012 | Original Article

Noise reduction in microarray gene expression data based on spectral analysis

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2012

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Abstract

In genetic research, microarray chip carries thousands of genome expression profiles which allow biologists to analyze some of the developmental processes of life, such as biological reactions due to specific influences and so on. A main challenge of DNA microarray analysis is to separate the main gene expression from experimental noise. In order to ensure the accuracy of the following analysis, an effective noise filtering scheme is needed. In this paper, we propose a strategy to remove noise from gene expression profiles based on an autoregressive model based power spectrum analysis combined with singular spectrum analysis. This method helps us to determine the power spectrum effectively such that we can easily reconstruct the noise filtered time series signal.

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Literature
1.
Bao F, Wang X, Tao Z, Wang Q, Du S (2009) Adaptive extraction of modulation for cavitation noise. J Acoust Soc Am 126:3106–3113CrossRef
2.
Bozdech Z, Llinas M, Pulliam BL, Wong ED, Zhu J, DeRisi JL (2003) The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLoS Biol 1:E5CrossRef
3.
4.
Choong MK, Charbit M, Yan H (2009) Autoregressive-model-based missing value estimation for dna microarray time series data. IEEE Trans Inf Technol Biomed 13(1):131–137CrossRef
5.
Choong MK, Levy D, Yan H (2009) Study of microarray time series data based on forward-backward linear prediction and singular value decomposition. Int J Data Min Bioinforma 3(2):145–159CrossRef
6.
Dequeant ML, Glynn E, Gaudenz K, Wahl M, Chen J, Mushegian A, Pourquie O (2006) A complex oscillating network of signaling genes underlies the mouse segmentation clock. Science 314(5805):1595–1598CrossRef
7.
Dequeant ML, Pourquie O (2008) Segmental patterning of the vertebrate embryonic axis. Nat Rev Genet 9(5):370–382CrossRef
8.
Draghici S (2003) Data analysis tools for DNA microarrays. Chapman & Hall/CRC, Boca RatonCrossRef
9.
Gan X, Liew AW, Yan H (2006) Microarray missing data imputation based on a set theoretic framework and biological knowledge. Nucleic Acids Res 34(5):1608–1619CrossRef
10.
Liu L, Hawkins DM, Ghosh S, Young SS (2003) Robust singular value decomposition analysis of microarray data. Proc Natl Acad Sci USA 100(23):13167–13172MathSciNetMATHCrossRef
11.
Mills JC, Roth KA, Cagan RL, Gordon JI (2001) Dna microarrays and beyond: completing the journey from tissue to cell. Nat Cell Biol 3(8):E175–E178CrossRef
12.
Ovacik MA, Sukumaran S, Almon RR, Dubois DC, Jusko WJ, Androulakis IP (2010) Circadian signatures in rat liver: from gene expression to pathways. BMC Bioinforma 11(1):540CrossRef
13.
Porat B (1994) Digital processing of random signals: theory and methods. Prentice-Hall, London
14.
Schott JR (2005) Matrix analysis for statistics, 2nd edn. Wiley series in probability and statistics. Wiley-Interscience, Hoboken
15.
Vautard R, Ghil M (1989) Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time-series. Physica D 35:395–424MathSciNetMATHCrossRef
16.
Wu S, Liew AW, Smith DK (2008) Spectral analysis of microarray gene expression time series data of Plasmodium falciparum. Int J Bioinforma Res Appl 4(3):337–349CrossRef
17.
Yeung LK, Szeto LK, Liew AW, Yan H (2004) Dominant spectral component analysis for transcriptional regulations using microarray time-series data. Bioinformatics 20(5):742–749CrossRef
Metadata
Title
Noise reduction in microarray gene expression data based on spectral analysis
Publication date
01-03-2012
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2012
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0039-7