Abstract
Microarrays allow the monitoring of thousands of genes simultaneously. Before a measure of gene activity of an organism is obtained, however, many stages in the error-prone manual and automated process have to be performed. Without quality control, the resulting measures may, instead of being estimates of gene activity, be due to noise or systematic variation. We address the problem of detecting spots of low quality from the microarray images to prevent them to enter the subsequent analysis. We extract features describing spatial characteristics of the spots on the microarray image and train a classifier using a set of labeled spots. We assess the results for classification of individual spots using ROC analysis and for a compound classification using a non-symmetric cost structure for misclassifications.
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References
T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M.H. Loh, J.R. Downing, M.A. Caligiuri, C.D. Bloomfield, and E.S. Lander. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286:531–537, 1999.
Yidong Chen, Edward R. Dougherty, and Michael L. Bittner. Ratio-based decisions and the quantitative analysis of cdna microarray images. Journal of Biomedical Optics, 1997.
Mei-Ling Ting Lee, Frank C. Kuo, G.A. Whitmore, and Jeffrey Sklar. Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetetive cdna hybridizations. Proc. Natl Acad. Sci. USA, 2000.
Yee Hwa Yang, Michael J. Buckley, Sandrine Dudoit, and Terence P. Speed. Comparison of methods for image analysis on cdna microarray data. Technical Report 584, Department of Statistics, University of California, Berkeley, December 2000.
Xujing Wang, Soumitra Ghosh, and Sun-Wei Guo. Quantitative quality control in microarray image processing and data acquisition. Nucleic Acids Research, 29(15), 2001.
Ron Dror. Noise models in gene array analysis. Report in fulfillment of the area exam requirement in the MIT Department of Electrical Engineering and Computer Science, 2001.
Mukund Thattai and Alexander van Oudenaarden. Intrinsic noise in gene regulatory networks. Proc. Natl Acad. Sci. USA, 2001.
Ertugrul M. Ozbudak, Iren Kurtser, Mukund Thattai, Alan D. Grossman, and Alexander van Ouderaarden. Regulation of noise in the expression of a singlegene. Nature Genetics, 2002.
Milan Sonka, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, 1993.
David Hand, Heikki Mannila, and Padhraic Smyth. Principles of Data Mining. Adaptive Computation and Machine Learning Series. MIT Press, 2001.
Richard O. Duda, Peter E. Hart, and David G. Stork. Pattern Classification. John Wiley & Sons, second edition, 2001.
J.P. Egan. Signal Detection Theory and ROC Analysis. New York: Academic Press, 1975.
John A. Swets. Measuring the accuracy of diagnostic systems. Science, 240:1285–1293, 1988.
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Ruosaari, S., Hollmén, J. (2002). Image Analysis for Detecting Faulty Spots from Microarray Images. In: Lange, S., Satoh, K., Smith, C.H. (eds) Discovery Science. DS 2002. Lecture Notes in Computer Science, vol 2534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36182-0_23
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DOI: https://doi.org/10.1007/3-540-36182-0_23
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