TAILIEUCHUNG - Báo cáo sinh học: " Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity. | Algorithms for Molecular Biology BioMed Central Open Access Ranking differentially expressed genes from Affymetrix gene expression data methods with reproducibility sensitivity and specificity Koji Kadota Yuji Nakai and Kentaro Shimizu Address Graduate School of Agricultural and Life Sciences The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-8657 Japan Email Koji Kadota - kadota@ Yuji Nakai - aynakai@ Kentaro Shimizu - shimizu@ Corresponding author Published 22 April 2009 Received 14 November 2008 Algorithms for Molecular Biology 2009 4 7 doi 1748-7188-4-7 Accepted 22 April 2009 This article is available from http content 4 1 7 2009 Kadota et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background To identify differentially expressed genes DEGs from microarray data users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expressionlevel measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However in addition to these recommendations researchers also want to know which combinations enhance reproducibility. Results We compared eight conventional methods for ranking genes weighted average difference WAD average difference AD fold change FC rank products RP moderated t statistic modT significance analysis of microarrays samT shrinkage t statistic shrinkT and intensitybased moderated t statistic ibmT with six preprocessing algorithms PLIER VSN FARMS multi-mgMOS mmgMOS MBEI and GCRMA

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