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Standardizing global gene expression analysis between laboratories and across platforms

Nature Methods volume 2, pages 351356 (2005) | Download Citation


  • An Addendum to this article was published on 01 June 2005


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.

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We thank J. Quackenbush from The Institute for Genomic Research, L. Hartwell from Fred Hutchinson Cancer Research Center and R. Wolfinger from the SAS Institute for their scientific contributions. We thank K.J. Yost (Science Applications International) and P. Cozart (NIEHS ITSS) for their information technology support. Research support was provided by National Institutes of Environmental Health Sciences grants ES11375, ES11384, ES11387, ES11391 and ES11399, and Contract # N01-ES-25497.

Author information


  1. Members of the Toxicogenomics Research Consortium

    A list of authors and their affiliations appears in the Supplementary Note


    Competing interests

    The author declare no competing financial interests.

    Corresponding author

    Correspondence to B.K. Weis.

    Supplementary information

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    1. 1.

      Supplementary Fig. 1

      Clustering of laboratory/platform combinations based on log ratio values associated with the common genes.

    2. 2.

      Supplementary Table 1

      Within and between laboratory median Pearson correlation coefficients of log intensities from standard array experiments.

    3. 3.

      Supplementary Table 2

      Within and between laboratory median Pearson correlation coefficients of log ratios (LvsP) for standard array experiments using different preprocessing.

    4. 4.

      Supplementary Table 3

      Common Gene Elements Across All Platforms (Standard and Resident Arrays): Mapping to NIA NAP Clusters.

    5. 5.

      Supplementary Table 4

      Percent overlap of significantly induced and repressed genes across laboratories for the Dataset D and Dataset C and number of gene transcripts identified as differentially expressed across laboratories for Dataset D and Dataset C.

    6. 6.

      Supplementary Table 5

      Percentage of the functionally-enriched GO Nodes that demonstrate different levels of concordance within and between branches of the clustering dendrogram.

    7. 7.

      Supplementary Methods

    8. 8.

      Supplementary Note

      Author list

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