Microarray standard data set and figures of merit for comparing data processing methods and experiment designs
Bioinformatics Vol. 19 no. 8 2003, Pages 956-965
Yudong D. He , Hongyue Dai , Eric E. Schadt , Guy Cavet , Stephen W. Edwards , Sergey B. Stepaniants , Sven Duenwald , Robert Kleinhanz , Allan R. Jones , Daniel D. Shoemaker and Roland B. Stoughton *
Rosetta Inpharmatics Inc.†,12 040 115th Avenue Northeast, Kirkland, WA 98034, USA
Received on July 24, 2002 ; revised on October 16, 2002 and December 17, 2002 ; accepted on December 22, 2002
Abstract
Motivation: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods.
Results: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the 24 000 60mer oligonucleotides that report 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change.
Supplementary Information PDF (file size: 223 KB)
Expression Data
* To whom correspondence should be addressed.
† A wholly owned subsidiary of Merck & Co., Inc.
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