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Standardizing global gene expression analysis between laboratories and across platforms

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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Figure 1: DNA microarray platforms used across laboratories.
Figure 2: Within- and between-center Pearson correlation coefficients for gene expression intensity using standard arrays.
Figure 3: Within and between laboratory Pearson correlation coefficients for log2 gene expression ratios using standard arrays.
Figure 4: Resident array Pearson correlation.
Figure 5: Sources of variation in gene expression measurements across microarray platforms and laboratories for resident arrays.
Figure 6: Clustering of 24 laboratory and platform combinations based on common GO nodes.

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Gene Expression Omnibus


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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.

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Correspondence to B.K. Weis.

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Supplementary information

Supplementary Fig. 1

Clustering of laboratory/platform combinations based on log ratio values associated with the common genes. (PDF 752 kb)

Supplementary Table 1

Within and between laboratory median Pearson correlation coefficients of log intensities from standard array experiments. (PDF 92 kb)

Supplementary Table 2

Within and between laboratory median Pearson correlation coefficients of log ratios (LvsP) for standard array experiments using different preprocessing. (PDF 84 kb)

Supplementary Table 3

Common Gene Elements Across All Platforms (Standard and Resident Arrays): Mapping to NIA NAP Clusters. (PDF 134 kb)

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. (PDF 91 kb)

Supplementary Table 5

Percentage of the functionally-enriched GO Nodes that demonstrate different levels of concordance within and between branches of the clustering dendrogram. (PDF 47 kb)

Supplementary Methods (PDF 718 kb)

Supplementary Note

Author list (PDF 63 kb)

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Members of the Toxicogenomics Research Consortium. Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2, 351–356 (2005).

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