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Báo cáo sinh học: "A weighted average difference method for detecting differentially expressed genes from microarray data"

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: A weighted average difference method for detecting differentially expressed genes from microarray data. | Algorithms for Molecular Biology BioMed Central Research A weighted average difference method for detecting differentially expressed genes from microarray data 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@bi.a.u-tokyo.ac.jp Yuji Nakai - yunakai@iu.a.u-tokyo.ac.jp Kentaro Shimizu - shimizu@bi.a.u-tokyo.ac.jp Corresponding author Open Access Published 26 June 2008 Received 4 December 2007 Algorithms for Molecular Biology 2008 3 8 doi 10.1186 1748-7188-3-8 Accepted 26 June 2008 This article is available from http www.almob.Org content 3 1 8 2008 Kadota et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Identification of differentially expressed genes DEGs under different experimental conditions is an important task in many microarray studies. However choosing which method to use for a particular application is problematic because its performance depends on the evaluation metric the dataset and so on. In addition when using the Affymetrix GeneChip system researchers must select a preprocessing algorithm from a number of competing algorithms such as MAS RMA and DFW for obtaining expression-level measurements. To achieve optimal performance for detecting DEGs a suitable combination of gene selection method and preprocessing algorithm needs to be selected for a given probe-level dataset. Results We introduce a new fold-change FC -based method the weighted average difference method WAD for ranking DEGs. It uses the average difference and relative average signal intensity so that highly expressed genes are highly ranked on the average for the different .