Rafael A Irizarry1, Zhijin Wu, Harris A Jaffee. 1. Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe Street, Baltimore, MD 21205, USA. rafa@jhu.edu
Abstract
MOTIVATION: In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was large and growing we developed a benchmark to help users identify the best method for their application. A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted. RESULTS: We benchmarked 31 probe set algorithms using a U95A dataset of spike in controls. Using this dataset, we found that background correction, one of the main steps in preprocessing, has the largest effect on performance. In particular, background correction appears to improve accuracy but, in general, worsen precision. The benchmark results put this balance in perspective. Furthermore, we have improved some of the original benchmark metrics to provide more detailed information regarding precision and accuracy. A handful of methods stand out as providing the best balance using spike-in data with the older U95A array, although different experiments on more current arrays may benchmark differently. AVAILABILITY: The affycomp package, now version 1.5.2, continues to be available as part of the Bioconductor project (http://www.bioconductor.org). The webtool continues to be available at http://affycomp.biostat.jhsph.edu CONTACT: rafa@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was large and growing we developed a benchmark to help users identify the best method for their application. A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted. RESULTS: We benchmarked 31 probe set algorithms using a U95A dataset of spike in controls. Using this dataset, we found that background correction, one of the main steps in preprocessing, has the largest effect on performance. In particular, background correction appears to improve accuracy but, in general, worsen precision. The benchmark results put this balance in perspective. Furthermore, we have improved some of the original benchmark metrics to provide more detailed information regarding precision and accuracy. A handful of methods stand out as providing the best balance using spike-in data with the older U95A array, although different experiments on more current arrays may benchmark differently. AVAILABILITY: The affycomp package, now version 1.5.2, continues to be available as part of the Bioconductor project (http://www.bioconductor.org). The webtool continues to be available at http://affycomp.biostat.jhsph.edu CONTACT: rafa@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Leming Shi; Laura H Reid; Wendell D Jones; Richard Shippy; Janet A Warrington; Shawn C Baker; Patrick J Collins; Francoise de Longueville; Ernest S Kawasaki; Kathleen Y Lee; Yuling Luo; Yongming Andrew Sun; James C Willey; Robert A Setterquist; Gavin M Fischer; Weida Tong; Yvonne P Dragan; David J Dix; Felix W Frueh; Frederico M Goodsaid; Damir Herman; Roderick V Jensen; Charles D Johnson; Edward K Lobenhofer; Raj K Puri; Uwe Schrf; Jean Thierry-Mieg; Charles Wang; Mike Wilson; Paul K Wolber; Lu Zhang; Shashi Amur; Wenjun Bao; Catalin C Barbacioru; Anne Bergstrom Lucas; Vincent Bertholet; Cecilie Boysen; Bud Bromley; Donna Brown; Alan Brunner; Roger Canales; Xiaoxi Megan Cao; Thomas A Cebula; James J Chen; Jing Cheng; Tzu-Ming Chu; Eugene Chudin; John Corson; J Christopher Corton; Lisa J Croner; Christopher Davies; Timothy S Davison; Glenda Delenstarr; Xutao Deng; David Dorris; Aron C Eklund; Xiao-hui Fan; Hong Fang; Stephanie Fulmer-Smentek; James C Fuscoe; Kathryn Gallagher; Weigong Ge; Lei Guo; Xu Guo; Janet Hager; Paul K Haje; Jing Han; Tao Han; Heather C Harbottle; Stephen C Harris; Eli Hatchwell; Craig A Hauser; Susan Hester; Huixiao Hong; Patrick Hurban; Scott A Jackson; Hanlee Ji; Charles R Knight; Winston P Kuo; J Eugene LeClerc; Shawn Levy; Quan-Zhen Li; Chunmei Liu; Ying Liu; Michael J Lombardi; Yunqing Ma; Scott R Magnuson; Botoul Maqsodi; Tim McDaniel; Nan Mei; Ola Myklebost; Baitang Ning; Natalia Novoradovskaya; Michael S Orr; Terry W Osborn; Adam Papallo; Tucker A Patterson; Roger G Perkins; Elizabeth H Peters; Ron Peterson; Kenneth L Philips; P Scott Pine; Lajos Pusztai; Feng Qian; Hongzu Ren; Mitch Rosen; Barry A Rosenzweig; Raymond R Samaha; Mark Schena; Gary P Schroth; Svetlana Shchegrova; Dave D Smith; Frank Staedtler; Zhenqiang Su; Hongmei Sun; Zoltan Szallasi; Zivana Tezak; Danielle Thierry-Mieg; Karol L Thompson; Irina Tikhonova; Yaron Turpaz; Beena Vallanat; Christophe Van; Stephen J Walker; Sue Jane Wang; Yonghong Wang; Russ Wolfinger; Alex Wong; Jie Wu; Chunlin Xiao; Qian Xie; Jun Xu; Wen Yang; Liang Zhang; Sheng Zhong; Yaping Zong; William Slikker Journal: Nat Biotechnol Date: 2006-09 Impact factor: 54.908
Authors: Richard Shippy; Stephanie Fulmer-Smentek; Roderick V Jensen; Wendell D Jones; Paul K Wolber; Charles D Johnson; P Scott Pine; Cecilie Boysen; Xu Guo; Eugene Chudin; Yongming Andrew Sun; James C Willey; Jean Thierry-Mieg; Danielle Thierry-Mieg; Robert A Setterquist; Mike Wilson; Anne Bergstrom Lucas; Natalia Novoradovskaya; Adam Papallo; Yaron Turpaz; Shawn C Baker; Janet A Warrington; Leming Shi; Damir Herman Journal: Nat Biotechnol Date: 2006-09 Impact factor: 54.908
Authors: Rafael A Irizarry; Christine Ladd-Acosta; Benilton Carvalho; Hao Wu; Sheri A Brandenburg; Jeffrey A Jeddeloh; Bo Wen; Andrew P Feinberg Journal: Genome Res Date: 2008-03-03 Impact factor: 9.043