Empirical evaluation of data transformations and ranking statistics for microarray analysis

Li Xuan Qin, Kathleen F. Kerr, Abee Boyles, Holly K. Dressman, Jonathan H. Freedman, Y. Ju Li, Renae L. Malek, David A. Schwartz, Susan Slifer, Mary C. Speer, Ivana Yang, Helmut Zarbl, Jung Lim Shin, Lichen Jing, Robert C. Sullivan, Rebecca Fry, Leona Samson, C. J. Tucker, R. D. Fannin, S. O. SieberJ. Li, P. R. Bushel, R. S. Paules, G. A. Boorman, M. L. Cunningham, B. K. Weis, Dongseok Choi, Jodi Lapidus, Michael Lasarev, Xinfang Lu, Jean O'Malley, Patrick Pattee, Srinivasa Nagalla, Signe Todd, Matthew Rodland, Peter Spencer, William Kaufmann, Charles Perou, Ivan Rusyn, James Swenberg, Blair Bradford, Shibing Deng, Terrance J. Kavanagh, Federico M. Farin, Richard P. Beyer, Sean Quigley, Theo K. Bammler

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


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 out-perform 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.

Original languageEnglish (US)
Pages (from-to)5471-5479
Number of pages9
JournalNucleic acids research
Issue number18
StatePublished - 2004

ASJC Scopus subject areas

  • Genetics


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