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Nucleic Acids Research 2004 32(18):5471-5479; doi:10.1093/nar/gkh866
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Published online 12 October 2004

Nucleic Acids Research, Vol. 32 No. 18 © Oxford University Press 2004; all rights reserved

Empirical evaluation of data transformations and ranking statistics for microarray analysis

Li-Xuan Qin and Kathleen F. Kerr* Contributing Members of the Toxicogenomics Research Consortium

Department of Biostatistics, University of Washington, F-600 Health Sciences Building 1705 NE Pacific Street, Box 357232, Seattle, WA 98195, USA

* To whom correspondence should be addressed. Tel: +1 206 543 1044; Fax: +1 206 543 3286: Email: katiek{at}u.washington.edu
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors
Contributing members of the Toxicogenomics Research Consortium: Duke University: Abee Boyles, Holly K. Dressman, Jonathan H. Freedman, Y-Ju Li, Renae L. Malek, David A. Schwartz, Susan Slifer, Marcy C. Speer and Ivana Yang; Fred Hutchinson Cancer Research Center: Helmut Zarbl, Jung-Lim Shin, Lichen Jing and Robert C. Sullivan; Massachusetts Institue of Technology: Rebecca Fry and Leona Samson; National Institute of Environmental Health Sciences: C. J. Tucker, R. D. Fannin, S. O. Sieber, J. Li, P. R. Bushel, R. S. Paules, G. A. Boorman, M. L. Cunningham and B. K. Weis; Oregon Health & Science University: Dongseok Choi, Jodi Lapidus, Michael Lasarev, Xinfang Lu, Jean O'Malley, Patrick Pattee, Srinivasa Nagalla, Signe Todd, Matthew Rodland and Peter Spencer; University of North Carolina at Chapel Hill: William Kaufmann, Charles Perou, Ivan Rusyn, James Swenberg, Blair Bradford and Shibing Deng; University of Washington: Terrance J. Kavanagh, Federico M. Farin, Richard P. Beyer, Sean Quigley and Theo K. Bammler.

Received August 26, 2004; Revised and Accepted September 15, 2004

There are many options in handling microarray data that can affect study conclusions, sometimes drastically. Working with a two-color platform, this study uses ten spike-in microarray experiments to evaluate the relative effectiveness of some of these options for the experimental goal of detecting differential expression. We consider two data transformations, background subtraction and intensity normalization, as well as six different statistics for detecting differentially expressed genes. Findings support the use of an intensity-based normalization procedure and also indicate that local background subtraction can be detrimental for effectively detecting differential expression. We also verify that robust statistics outperform t-statistics in identifying differentially expressed genes when there are few replicates. Finally, we find that choice of image analysis software can also substantially influence experimental conclusions.


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