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The Role of Combined OSR and SDF Method for Pre-Processing of Microarray Data that Accounts for Effective Denoising and Quantification

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DOI: 10.4236/jsip.2011.23026    4,202 Downloads   6,949 Views   Citations


Microarray data is inherently noisy due to the noise contaminated from various sources during the preparation of microarray slide and thus it greatly affects the accuracy of the gene expression. How to eliminate the effect of the noise constitutes a challenging problem in microarray analysis. Efficient denoising is often a necessary and the first step to be taken before the image data is analyzed to compensate for data corruption and for effective utilization for these data. Hence preprocessing of microarray image is an essential to eliminate the background noise in order to enhance the image quality and effective quantification. Existing denoising techniques based on transformed domain have been utilized for microarray noise reduction with their own limitations. The objective of this paper is to introduce novel preprocessing techniques such as optimized spatial resolution (OSR) and spatial domain filtering (SDF) for reduction of noise from microarray data and reduction of error during quantification process for estimating the microarray spots accurately to determine expression level of genes. Besides combined optimized spatial resolution and spatial filtering is proposed and found improved denoising of microarray data with effective quantification of spots. The proposed method has been validated in microarray images of gene expression profiles of Myeloid Leukemia using Stanford Microarray Database with various quality measures such as signal to noise ratio, peak signal to noise ratio, image fidelity, structural content, absolute average difference and correlation quality. It was observed by quantitative analysis that the proposed technique is more efficient for denoising the microarray image which enables to make it suitable for effective quantification.

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The authors declare no conflicts of interest.

Cite this paper

J. Meher, M. Raval, P. Meher and G. Dash, "The Role of Combined OSR and SDF Method for Pre-Processing of Microarray Data that Accounts for Effective Denoising and Quantification," Journal of Signal and Information Processing, Vol. 2 No. 3, 2011, pp. 190-195. doi: 10.4236/jsip.2011.23026.


[1] T. K. Attwood and D. J. Parry-Smith, “Introduction to Bioinformatics,” Addison Wesley Longman Limited, Harlow, 1999.
[2] R. C. Y. Cheung and C. J. S. Desilva, “Analysis of Gene Microarray Image,” Neural Information Processing, Proceedings of ICONIP’99, 6th International Conference, Perth, Vol. 2, November 1999, pp. 627-632.
[3] R. S. H. Istepanian, “Microarray Image Processing: Current Status & Future Directions,” IEEE Transactions on NanoBioScience, Vol. 2, No. 4, December 2003, pp. 173- 175.
[4] J. Dopazo, “Microarray Data Processing and Analysis,” Kluwer Academic Publisher, Boston, 2002, pp. 43-63.
[5] D. Vijayan and A. S. Nair, “Microarray Image Processing: Spot Detection, Quantization and Clustering,” Center for Bioinformatics, KU, March 2006.
[6] B. Alhadidi, H. N. Fakhouri and O. S. AlMousa, “cDNA Microarray Genome Image Processing Using Fixed Spot Position,” American Journal of Applied Sciences, Vol. 3, No. 2, 2006, pp. 1730-1734.
[7] S. H. Ni, P. Wang, et al., “Spotted cDNA Microarray Image Segmentation Using ACWE,” Romanian Journal of Information Science and Technology, Vol. 12, No. 2, 2009, p. 249.
[8] A. Sreedevi and D. S. angamashetti, “Automatically Locating Spots in DNA Microarray Image Using Genetic Algorithm without Gridding,” IEEE IACSIT Spring Conference, Singapore, 17-20 April 2009, pp. 178-181.
[9] J. Buhler, T. Ideker and D. Haynor, “Dapple: Improved Techniques for Finding Sports on DNA Microarrays,” Technical Reports UWTR 2000-08-05, University of Washington, 2000.
[10] A. N. Jain, T. A. Tokuyasu, A. M. Snijders, R. Segraves, D. G. Albertson and D. Pinkel1, “Fully Automatic Quantification of Microarray Image Data,” Genome Research, Vol. 12, No. 2, February 2002, pp. 325-332. doi:10.1101/gr.210902
[11] T. Tokuyasu, D. Albertson, D. Pinkel and A. Jain, “Wavelet Transforms for the Analysis of Microarray Experiments,” Proceedings of IEEE Computer Society Bioinformatics Conference, 11-14 August 2003, pp. 429-430.
[12] X. H. Wang, R. S. H. Istepanian and Y. H. Song, “Microarray Image Enhancement by Denoising Using Stationary Wavelet Transform,” Transactions on Nanobioscience, Vol. 2, No. 4, December 2003, pp. 184-189.
[13] H. Stefanou1, T. Margaritis, D. Kafetzopoulos, K. Marias and P. Tsakalides, “Microarray Image Denoising Using a Two-Stage Multiresolution Technique,” IEEE International Conference on Bioinformatics and Biomedicine, 2007, pp. 383-389.
[14] A. Zifan, M. H. Moradi and S. Gharibzadeh, “Microarray Image Enhancement by Denoising Using Decimated and Undecimated Multiwavelet Transforms,” Signal, Image and Video processing, Vol. 4, No. 2, 2009, pp. 177-185.
[15] A. Mastrogianni, E. Dermatas and A. Bezerianos, “Microarray Image Denoising Using Spatial Filtering and Wavelet Transformation,” IFMBE Proceedings, Vol. 23, No. 1, 2009, pp. 594-597.
[16] R. C. Gonzalez, R. E. Wood and S. L. Eddins, “Digital Image Processing Using Matlab,” Pearson Education, Inc., Berkeley, 2004.
[17] J. Gollub, C. A. Ball, G. Binkley, et al., “The Stanford Microarray Database: Data Access and Quality Assessment Tools,” Nucleic Acids Research, Vol. 31, No. 1, 2003, pp. 94-96.
[18] C. A. Ball, I. A. B. Awad, J. Demeter, et al., “The Stanford Microarray Database Accommodates Additional Microarray Platforms and Data Formats,” Nucleic Acids Research, Vol. 33, Database Issue, 2005, pp. D580-D582.
[19] M. Mastriani and A. E. Giraldez, “Microarrays Denoising via Smoothing of Coefficients in Wavelet Domain,” International Journal of Biomedical Sciences, Vol. 1, No. 1, 2006, pp. 7-14.

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