Abstract
The DNA microarray analysis is one of the most important areas in biomedical research. For the accurate analysis of microarray data the process of segmentation, classification of pixels as foreground or background, should be done accurately. In this paper we suggest a kernel density estimation approach for the segmentation of the microarray spot. We estimate the density of n pixel intensities for a given target area by the kernel density estimation, and the resulting kernel density estimate gives bimodal density by appropriate choice of the smoothing parameter. We suggest two modes of the kernel density estimate for n pixel intensities as estimates of the foreground (mode with larger value) and the background (mode with smaller value) intensity, respectively. The segmentation method proposed in this paper is easy and simple to use, robust to the shape of spot, and very accurate.
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References
Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16: 641–647
Axon Instruments Inc. (1999) GenePix 4000A User’s Guide. http://www.axon.com
Beucher S, Meyer F (1993) The morphological approach to segmentation: the watershed transformation. In: mathematical morphology in image processing. Optical engineering, vol 34. Marcel Dekker, New York, pp 433–481
Bozinov D, Rahnenführer J (2002) Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics 18:747–756
Buckley MJ (2000) The Spot User’s Guide, CSIRO mathematical and information sciences. http://www. cmis.csiro.au/IAP/Spot/spotmanual.htm
Chen Y, Dougherty ER, Bittner ML (1997) Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed Opt 2:364–374
Chen Y, Kamat V, Dougherty ER, Bittner ML, Meltzer PS, Trent JM (2002) Ratio statistics of gene expression levels and applications to microarray data analysis. Bioinformatics 18:1207–1215
Edwards D (2003) Non-linear normalization and background correction in one-channel cDNA microarray studies. Bioinformatics 19:825–833
Eisen MB (1999) ScanAlyze. http://rana.Stanford.edu/software
Goryachev AB, Macgregor PF, Edwards AM (2001) Unfolding of microarray data. J Comput Biol 8:443–461
GSI Lumonics (1999) QuantArray analysis software, Operator’s Manual
Ihaka R, Gentleman R (1996) R: A language for data analysis and graphics. J Comput Graph Stat 5:299–314
Kooperberg C, Fazzio TG, Delrow JJ, Tsukiyama T (2002) Improved background correction for spotted DNA microarrays. J Comput Biol 9:55–66
Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnol 14:1675–1680
Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470
Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, London
Vincent L, Soille P (1991) Watershed in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13:583–598
Wand MP, Jones MC (1995) Kernel smoothing. Chapman and Hall, London
Yang YH, Buckley MJ, Dudoit S, Speed TP (2002) Comparison of methods for image analysis on cDNA microarray data. J Comput Graph Stat 11:108–136
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This research was supported by Korea Science and Engineering Foundation grant (R14-2003-002-01000-0).
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Bae, W., Kim, C. A simple segmentation method for DNA microarray spots by kernel density estimation. OR Spectrum 30, 223–234 (2008). https://doi.org/10.1007/s00291-007-0091-6
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DOI: https://doi.org/10.1007/s00291-007-0091-6