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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Bayesian Hierarchical Model for Estimating Gene Expression Intensity Using Multiple Scanned Microarrays | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2008 Article ID 231950 11 pages doi 10.1155 2008 231950 Research Article Bayesian Hierarchical Model for Estimating Gene Expression Intensity Using Multiple Scanned Microarrays Rashi Gupta 1 2 Elja Arjas 1 3 Sangita Kulathinal 1 Andrew Thomas 1 and Petri Auvinen2 1 Department of Mathematics and Statistics University of Helsinki P.O. Box 68 00014 Helsinki Finland 2 Institute of Biotechnology University of Helsinki P.O.Box 56 00014 Helsinki Finland 3National Public Health Institute KTL Mannerheimintie 166 00300 Helsinki Finland Correspondence should be addressed to Rashi Gupta rashi1@live.com Received 7 July 2007 Accepted 28 November 2007 Recommended by Yufei Huang We propose a method for improving the quality of signal from DNA microarrays by using several scans at varying scanner sensitivities. A Bayesian latent intensity model is introduced for the analysis of such data. The method improves the accuracy at which expressions can be measured in all ranges and extends the dynamic range of measured gene expression at the high end. Our method is generic and can be applied to data from any organism for imaging with any scanner that allows varying the laser power and for extraction with any image analysis software. Results from a self-self hybridization data set illustrate an improved precision in the estimation of the expression of genes compared to what can be achieved by applying standard methods and using only a single scan. Copyright 2008 Rashi Gupta et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction DNA microarray technology is used to study simultaneously the expression profile of a large number of distinct genes 1 . Several factors contribute to the accuracy with which these genes and their .
