Standardizing global gene expression analysis between laboratories and across platforms

Theodore Bammler, Richard P. Beyer, Sanchita Bhattacharya, Gary A. Boorman, Abee Boyles, Blair U. Bradford, Roger E. Bumgarner, Pierre R. Bushel, Kabir Chaturvedi, Dongseok Choi, Michael L. Cunningham, Shibing Deng, Holly K. Dressman, Rickie D. Fannin, Fredrico M. Farin, Jonathan H. Freedman, Rebecca C. Fry, Angel Harper, Michael C. Humble, Patrick HurbanTerrance J. Kavanagh, William K. Kaufmann, Kathleen F. Kerr, Li Jing, Jodi A. Lapidus, Michael R. Lasarev, Jianying Li, Yi Ju Li, Edward K. Lobenhofer, Xinfang Lu, Renae L. Malek, Sean Milton, Srinivasa R. Nagalla, Jean P. O'Malley, Valerie S. Palmer, Patrick Pattee, Richard S. Paules, Charles M. Perou, Ken Phillips, Li Xuan Qin, Yang Qiu, Sean D. Quigley, Matthew Rodland, Ivan Rusyn, Leona D. Samson, David A. Schwartz, Yan Shi, Jung Lim Shin, Stella O. Sieber, Susan Slifer, Marcy C. Speer, Peter S. Spencer, Dean I. Sproles, James A. Swenberg, William A. Suk, Robert C. Sullivan, Ru Tian, Raymond W. Tennant, Signe A. Todd, Charles J. Tucker, Bennett Van Houten, Brenda K. Weis, Shirley Xuan, Helmut Zarbl

Research output: Contribution to journalArticle

386 Scopus citations

Abstract

To facilitate collaborative research efforts between multi-investigator teams using DNA microarrays, we identified sources of error and data variability between laboratories and across microarray platforms, and methods to accommodate this variability. RNA expression data were generated in seven laboratories, which compared two standard RNA samples using 12 microarray platforms. At least two standard microarray types (one spotted, one commercial) were used by all laboratories. Reproducibility for most platforms within any laboratory was typically good, but reproducibility between platforms and across laboratories was generally poor. Reproducibility between laboratories increased markedly when standardized protocols were implemented for RNA labeling, hybridization, microarray processing, data acquisition and data normalization. Reproducibility was highest when analysis was based on biological themes defined by enriched Gene Ontology (GO) categories. These findings indicate that microarray results can be comparable across multiple laboratories, especially when a common platform and set of procedures are used.

Original languageEnglish (US)
Pages (from-to)1-6
Number of pages6
JournalNature Methods
Volume2
Issue number5
DOIs
StatePublished - May 1 2005

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ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology

Cite this

Bammler, T., Beyer, R. P., Bhattacharya, S., Boorman, G. A., Boyles, A., Bradford, B. U., Bumgarner, R. E., Bushel, P. R., Chaturvedi, K., Choi, D., Cunningham, M. L., Deng, S., Dressman, H. K., Fannin, R. D., Farin, F. M., Freedman, J. H., Fry, R. C., Harper, A., Humble, M. C., ... Zarbl, H. (2005). Standardizing global gene expression analysis between laboratories and across platforms. Nature Methods, 2(5), 1-6. https://doi.org/10.1038/NMETH754