Abstract
A spot-adaptive compound clustering-enhancement-segmentation (CES) scheme was developed for the quantification of gene expression levels in microarray images. The CES-scheme employed 1/griding, for locating spot-regions, 2/Fuzzy C-means clustering, for segmenting spots from background, 3/ background noise estimation and spot’s center localization, 4/emphasizing of spot’s outline by the CLAHE image enhancement technique, 5/segmentation by the SRG algorithm, using information from step 3, and 6/microarray spot intensity extraction. Extracted intensities by the CES-Scheme were compared against those obtained by the MAGIC TOOL’s SRG. Kullback-Liebler metric’s values for the CES-Scheme were on average double than MAGIC TOOL’s, with differences ranging from 1.45bits to 2.77bits in 7 cDNA images. Coefficient-of-Variation results showed significantly higher reproducibility (p<0.001) for the CES-Scheme in quantifying gene expression levels. Processing times for 1024x1024 16-bit microarray images containing 6400 spots were 300 and 487 seconds for the CES-Scheme and MAGIC TOOL respectively.
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References
Alizadeh, A., Eisen, M., Botstein, D., Brown, P.O., Staudt, L.M.: Probing lymphocyte biology by genomic-scale gene expression analysis. J. Clin. Immunol. 18, 373–379 (1998)
Churchill, G.A.: Fundamentals of experimental design for cdna microarrays. Nat Genet. 32, 490–495 (2002)
Taniguchi, M., Miura, K., Iwao, H., Yamanaka, S.: Quantitative assessment of DNA microarrays–comparison with northern blot analyses. Genomics 71, 34–39 (2001)
Schena, M.: Microarray biochip technology, 1st edn. Eaton Publishing Company (2000)
Chen, Y., Dougherty, E., Bittner, M.: Ratio-based decisions and the quantitative analysis of cdna microarray images. Journal of Biomedical Optics 2, 364–374 (1997)
Schena, M.: Microarray analysis, 1st edn. New York (2002)
Jain, A.N., Tokuyasu, T.A., Snijders, A.M., Segraves, R., Albertson, D.G., Pinkel, D.: Fully automatic quantification of microarray image data. Genome Res. 12, 325–332 (2002)
Yang, Y.H., Buckley, M.J., Speed, T.P.: Analysis of cdna microarray images. Brief Bioinform. 2, 341–349 (2001)
Schuchhardt, J., Beule, D., Malik, A., Wolski, E., Eickhoff, H., Lehrach, H., Herzel, H.: Normalization strategies for cdna microarrays. Nucleic Acids Res. 28, 47 (2000)
Balagurunathan, Y., Wang, N., Dougherty, E.R., Nguyen, D., Chen, Y., Bittner, M.L., Trent, J., Carroll, R.: Noise factor analysis for cdna microarrays. J. Biomed. Opt. 9, 663–678 (2004)
Balagurunathan, Y., Dougherty, E.R., Chen, Y., Bittner, M.L., Trent, J.M.: Simulation of cdna microarrays via a parameterized random signal model. J. Biomed. Opt. 7, 507–523 (2002)
Ahmed, A.A., Vias, M., Iyer, N.G., Caldas, C., Brenton, J.D.: Microarray segmentation methods significantly influence data precision. Nucleic Acids Res. 32, e50 (2004)
Gonzalez, R.C., Woods, R.E.: Digital image processing, 1st edn (1992)
Axon Instruments. Genepix4000a user’s guide (1999)
Steinfath, M., Wruck, W., Seidel, H., Lehrach, H., Radelof, U., O’Brien, J.: Automated image analysis for array hybridization experiments. Bioinformatics 17, 634–641 (2001)
White, A.M., Daly, D.S., Willse, A.R., Protic, M., Chandler, D.P.: Automated microarray image analysis toolbox for matlab. Bioinformatics 21, 3578–3579 (2005)
Zapala, M.A., Lockhart, D.J., Pankratz, D.G., Garcia, A.J., Barlow, C., Lockhart, D.J.: Software and methods for oligonucleotide and cdna array data analysis. Genome Biol. 3, 1 (2002)
QuantArray Analysis Software, O.s.M. Available: via the INTERNET. Accessed
Eisen, M.B.S.: (Accessed 06/12/2006) via the INTERNET, Available: http://rana.stanford.edu/software
Wang, X.H., Istepanian, R.S., Song, Y.H.: Microarray image enhancement by denoising using stationary wavelet transform. IEEE Trans Nanobioscience 2, 184–189 (2003)
Lukac, R., Plataniotis, K.N., Smolka, B., Venetsanopoulos, A.N.: Cdna microarray image processing using fuzzy vector filtering framework. Journal of Fuzzy Sets and Systems: Special Issue on Fuzzy Sets and Systems in Bioinformatics (2005)
Mastriani, M., Giraldez, A.E.: Microarrays denoising via smoothing of coefficients in wavelet domain. International Journal of Biomedical Sciences 1, 1306–1316 (2006)
Lukac, R., Smolka, B.: Application of the adaptive center-weighted vector median framework for the enhancement of cdna microarray. Int. J. Appl. Math. Comput. Sci. 13, 369–383 (2003)
Daskalakis, A., Cavouras, D., Bougioukos, P., Kostopoulos, S., Argyropoulos, C., Nikiforidis, G.C.: Improving microarray spots segmentation by k-means driven adaptive image restoration. In: Proceedings of the ITAB Ioannina, Greece (2006)
Jain, A.K.: Fundamentals of digital image processing. Prentice-Hall, Englewood Cliffs (1989)
Adams, R., Bischof, L.: Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641–647 (1994)
Kullback, S.: Information theory and statistics, 2nd edn. Dover Publications, Mineola (1968)
Heyer, L.J., Moskowitz, D.Z., Abele, J.A., Karnik, P., Choi, D., Campbell, A.M., Oldham, E.E., Akin, B.K.: Magic tool: Integrated microarray data analysis. Bioinformatics 21, 2114–2115 (2005)
(Accessed 06/12/2006) Available: via the INTERNET, http://www.bio.davidson.edu/projects/MAGIC/MAGIC.html
DeRisi, J.L., Iyer, V.R., Brown, P.O.: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997)
Sterrenburg, E., Turk, R., Boer, J.M., van Ommen, G.B., den Dunnen, J.T.: A common reference for cdna microarray hybridizations. Nucleic Acids Res. 30, e116 (2002)
Yang, Y.H., Speed, T.: Design issues for cdna microarray experiments. Nat. Rev. Genet. 3, 579–588 (2002)
Cleveland, W.S.: Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 829–836 (1979)
Pizer, S.M., Amburn, E.P.: Adaptive histogram equalization and its variations. Grpahics, and Image Processing 39, 355–368 (1987)
Bowman, A.W., Azzalini, A.: Applied smoothing techniques for data analysis. Oxford University Press, Oxford (1997)
Nykter, M., Aho, T., Ahdesmaki, M., Ruusuvuori, P., Lehmussola, A., Yli-Harja, O.: Simulation of microarray data with realistic characteristics. BMC Bioinformatics 7, 349 (2006)
Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97, 77 (2002)
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Daskalakis, A. et al. (2007). Effective Quantification of Gene Expression Levels in Microarray Images Using a Spot-Adaptive Compound Clustering-Enhancement-Segmentation Scheme. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4707. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74484-9_48
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DOI: https://doi.org/10.1007/978-3-540-74484-9_48
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