ABSTRACT
One of the main issues of the analysis of microarray data is quantification of gene expression. The quantified signal intensities should be linearly related to the expression levels of the corresponding genes. In this paper, we present a biological assessment for detection and segmentation of grids and spots, and quantification of gene expression in cDNA microarray images. The results on several dilution steps on cDNA microarray images show that the proposed method can detect the location of the spots very effectively even for noisy conditions based on a parameterless multilevel thresholding algorithm. The proposed method can also segment and quantify the intensity of each probe with a nearly perfect degree of accuracy. This guarantees that the proposed method estimates the correct intensity of each spot with a high degree of accuracy and relates it to the expression levels of the corresponding genes very well.
- B. Ceccarelli and G. Antoniol. A Deformable Grid-matching Approach for Microarray Images. IEEE Transactions on Image Processing, 15(10):3178--3188, 2006. Google ScholarDigital Library
- D. Bariamis, D. Maroulis and D. Iakovidis. M3G: Maximum Margin Microarray Gridding. BMC Bioinformatics, 11:49, 2010.Google ScholarCross Ref
- E. Zacharia and D. Maroulis. Micoarray image gridding via an evolutionary algorithm. IEEE International Conference on Image Processing, pages 1444--1447, 2008.Google Scholar
- F. Qi, Y. Luo, and D. Hu. Recognition of perspectively distorted planar grids. Pattern Recognition Letters, 27(14):1725--1731, 2006. Google ScholarDigital Library
- G. Antoniol and M. Ceccarelli. A Markov Random Field Approach to Microarray Image Gridding. Proc. of the 17th International Conference on Pattern Recognition, pages 550--553, 2004. Google ScholarDigital Library
- D. Hekstra, A. Taussig, M. Magnasco, and F. Naef. Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide arrays. Nucleic Acids Res., 31(7):1962--1968, 2003.Google ScholarCross Ref
- J. Angulo and J. Serra. Automatic Analysis of DNA Microarray Images Using Mathematical Morphology. Bioinformatics, 19(5):553--562, 2003.Google ScholarCross Ref
- J. Canny. A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679--698, 1986. Google ScholarDigital Library
- L. Rueda. Sub-grid Detection in DNA Microarray Images. Proceedings of the IEEE Pacific-RIM Symposium on Image and Video Technology, pages 248--259, 2007. Google ScholarDigital Library
- L. Rueda and V. Vidyadharan. A Hill-climbing Approach for Automatic Gridding of cDNA Microarray Images. IEEE Transactions on Computational Biology and Bioinformatics, 3(1):72--83, 2006. Google ScholarDigital Library
- M. Luessi, M. Eichmann, M. Shuster, and A. Katsaggelos. Framework for efficient optimal multilevel image thresholding. Journal of Electronic Imaging, 18(1):013004+, 2009.Google Scholar
- M. Katzer, F. Kummer, and G. Sagerer. A Markov Random Field Model of Microarray Gridding. Proceeding of the 2003 ACM Symposium on Applied Computing, pages 72--77, 2003. Google ScholarDigital Library
- L. Ramdas, K. R. Coombes, K. Baggerly, L. Abruzzo, W. E. Highsmith, T. Krogmann, S. R. Hamilton, and W. Zhang. Sources of nonlinearity in cdna microarray expression measurements. Genome Biology, 2(11), 2001.Google Scholar
- I. Rezaeian and L. Rueda. A Parameterless Automatic Spot Detection Method for cDNA Microarray Images. Proceedings of IEEE International Conference on Bioinformatics and Biomedicine, pages 388--392, 2010.Google ScholarCross Ref
- L. Rueda. An Efficient Algorithm for Optimal Multilevel Thresholding of Irregularly Sampled Histograms. Proceedings of the 7th International Workshop on Statistical Pattern Recognition, pages 612--621, 2008. Google ScholarDigital Library
- S. Theodoridis and K. Koutroumbas. Pattern Recognition. Elsevier Academic Press, fourth edition, 2008. Google ScholarDigital Library
- U. Maulik and S. Bandyopadhyay. Performance Evaluation of Some Clustering Algorithms and Validity Indices. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(12):1650--1655, 2002. Google ScholarDigital Library
- Xiuwen Zheng, Hung-Chung Huang, Wenyuan Li, Peng Liu, Quan-Zhen Li and Ying Liu. Modeling nonlinearity in dilution design microarray data. Bioinformatics, 23(11):1339--1347, 2007. Google ScholarDigital Library
Index Terms
- Biological assessment of grid and spot detection in cDNA microarray images
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