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Environmental Health Perspectives logoLink to Environmental Health Perspectives
. 2004 Mar;112(4):449–455. doi: 10.1289/ehp.6787

Cross-site comparison of gene expression data reveals high similarity.

Tzu-Ming Chu 1, Shibing Deng 1, Russ Wolfinger 1, Richard S Paules 1, Hisham K Hamadeh 1
PMCID: PMC1241898  PMID: 15033594

Abstract

Consistency and coherence of gene expression data across multiple sites depends on several factors such as platform (oligo, cDNA, etc.), environmental conditions at each laboratory, and data quality. The Hepatotoxicity Working Group of the International Life Sciences Institute Health and Environmental Sciences Institute consortium on the application of genomics to mechanism-based risk assessment is investigating these factors by comparing high-density gene expression data sets generated on two sets of RNA from methapyrilene (MP) experiments conducted at Abbott Laboratories and Boehringer-Ingelheim Pharmaceuticals, Inc. using a single platform (Affymetrix Rat Genome U34A GeneChip) at seven different sites. This article focuses on the evaluation of data quality and statistical models that facilitate the comparison of such data sets at the probe level. We present methods for exploring and quantitatively assessing differences in the data, with the principal goal being the generation of lists of site-insensitive genes responsive to low and high doses of MP. A combination of numerical and graphical techniques reveals important patterns and partitions of variability in the data, including the magnitude of the site effects. Although the site effects are significantly large in the analysis results, they appear to be primarily additive and therefore can be adjusted in the statistical calculations in a way that does not bias conclusions regarding treatment differences.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.

  1. Bolstad B. M., Irizarry R. A., Astrand M., Speed T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003 Jan 22;19(2):185–193. doi: 10.1093/bioinformatics/19.2.185. [DOI] [PubMed] [Google Scholar]
  2. Choi Jung Kyoon, Yu Ungsik, Kim Sangsoo, Yoo Ook Joon. Combining multiple microarray studies and modeling interstudy variation. Bioinformatics. 2003;19 (Suppl 1):i84–i90. doi: 10.1093/bioinformatics/btg1010. [DOI] [PubMed] [Google Scholar]
  3. Chu Tzu Ming, Weir Bruce, Wolfinger Russ. A systematic statistical linear modeling approach to oligonucleotide array experiments. Math Biosci. 2002 Mar;176(1):35–51. doi: 10.1016/s0025-5564(01)00107-9. [DOI] [PubMed] [Google Scholar]
  4. Ghosh Debashis, Barette Terrence R., Rhodes Dan, Chinnaiyan Arul M. Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer. Funct Integr Genomics. 2003 Jul 22;3(4):180–188. doi: 10.1007/s10142-003-0087-5. [DOI] [PubMed] [Google Scholar]
  5. Irizarry Rafael A., Hobbs Bridget, Collin Francois, Beazer-Barclay Yasmin D., Antonellis Kristen J., Scherf Uwe, Speed Terence P. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003 Apr;4(2):249–264. doi: 10.1093/biostatistics/4.2.249. [DOI] [PubMed] [Google Scholar]
  6. Li C., Wong W. H. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci U S A. 2001 Jan 2;98(1):31–36. doi: 10.1073/pnas.011404098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Lockhart D. J., Dong H., Byrne M. C., Follettie M. T., Gallo M. V., Chee M. S., Mittmann M., Wang C., Kobayashi M., Horton H. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol. 1996 Dec;14(13):1675–1680. doi: 10.1038/nbt1296-1675. [DOI] [PubMed] [Google Scholar]
  8. Schadt E. E., Li C., Ellis B., Wong W. H. Feature extraction and normalization algorithms for high-density oligonucleotide gene expression array data. J Cell Biochem Suppl. 2001;Suppl 37:120–125. doi: 10.1002/jcb.10073. [DOI] [PubMed] [Google Scholar]
  9. Tan Paul K., Downey Thomas J., Spitznagel Edward L., Jr, Xu Pin, Fu Dadin, Dimitrov Dimiter S., Lempicki Richard A., Raaka Bruce M., Cam Margaret C. Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res. 2003 Oct 1;31(19):5676–5684. doi: 10.1093/nar/gkg763. [DOI] [PMC free article] [PubMed] [Google Scholar]

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