Standardizing global gene expression analysis between laboratories and across platforms - PubMed (original) (raw)
doi: 10.1038/nmeth754. Epub 2005 Apr 21.
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 Hurban, Terrance 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; Members of the Toxicogenomics Research Consortium
- PMID: 15846362
- DOI: 10.1038/nmeth754
Standardizing global gene expression analysis between laboratories and across platforms
Theodore Bammler et al. Nat Methods. 2005 May.
Erratum in
- Nat Methods. 2005 Jun;2(6):477
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.
Comment in
- Of fish and chips.
Sherlock G. Sherlock G. Nat Methods. 2005 May;2(5):329-30. doi: 10.1038/nmeth0505-329. Nat Methods. 2005. PMID: 15846357 No abstract available.
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