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Rat toxicogenomic study reveals analytical consistency across microarray platforms

Nature Biotechnology volume 24, pages 1162–1169 (2006)Cite this article

Abstract

To validate and extend the findings of the MicroArray Quality Control (MAQC) project, a biologically relevant toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine and comfrey) and each sample was hybridized to four microarray platforms. The MAQC project assessed concordance in intersite and cross-platform comparisons and the impact of gene selection methods on the reproducibility of profiling data in terms of differentially expressed genes using distinct reference RNA samples. The real-world toxicogenomic data set reported here showed high concordance in intersite and cross-platform comparisons. Further, gene lists generated by fold-change ranking were more reproducible than those obtained by t-test P value or Significance Analysis of Microarrays. Finally, gene lists generated by fold-change ranking with a nonstringent P-value cutoff showed increased consistency in Gene Ontology terms and pathways, and hence the biological impact of chemical exposure could be reliably deduced from all platforms analyzed.

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Figure 1: Hierarchical clustering of platform-specific microarray data separates samples by tissue and treatment.
Figure 2: Hierarchical clustering of all individual sample data from all microarray platforms separated by tissue and treatment.
Figure 3: Intralaboratory overlap of differentially expressed gene lists generated using different selection criteria.
Figure 4: Intralaboratory overlap of enriched GO terms.
Figure 5: Intralaboratory overlap of differentially enriched KEGG pathways.

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Acknowledgements

E.K.L., K.L.P. and P.H. acknowledge Agilent Technologies, Inc. and Affymetrix, Inc. for their material contributions to this work, thank John Pufky, Stephen Burgin and Jennifer Troehler for their outstanding technical assistance, and gratefully acknowledge the Advanced Technology Program of the National Institute of Standards and Technology, whose generous support provided partial funding of this research (70NANB2H3009). C.W. acknowledges Affymetrix, Inc. for material contributions to this work. R.S. acknowledges technical support of Alan Brunner for generating GE Healthcare microarray data. L.G. and L.S. thank X. Megan Cao, Stacey Dial, Carrie Moland and Feng Qian for their superb technical assistance.

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Authors and Affiliations

  1. National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, 72079, Arkansas, USA

    Lei Guo, Stephen C Harris, Nan Mei, Tao Chen, Weida Tong, Yvonne P Dragan & Leming Shi

  2. A Division of Clinical Data, Cogenics, 100 Perimeter Park Drive, Suite C, Morrisville, 27560, North Carolina, USA

    Edward K Lobenhofer, Patrick Hurban & Kenneth L Phillips

  3. UCLA David Geffen School of Medicine, Transcriptional Genomics Core, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, 90048, California, USA

    Charles Wang, Jun Xu & Xutao Deng

  4. GE Healthcare, 7700 S. River Parkway, Suite #2603, Tempe, 85284, Arizona, USA

    Richard Shippy

  5. Solexa, 25861 Industrial Boulevard, Hayward, 94545, California, USA

    Lu Zhang

  6. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894, Maryland, USA

    Damir Herman

  7. Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20993, Maryland, USA

    Federico M Goodsaid

  8. Applied Biosystems, 850 Lincoln Centre Drive, Foster City, 94404, California, USA

    Yongming Andrew Sun

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Corresponding authors

Correspondence to Lei Guo or Leming Shi.

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Competing interests

R.S., L.Z. and Y.A.S. declare competing interests on funding. The other authors declare no competing interests.

Supplementary information

Supplementary Fig. 1

Principal component analysis of the platform-specific microarray data separates samples by tissue and treatment. (PDF 203 kb)

Supplementary Fig. 2

Inter-site overlap of differentially expressed gene lists generated using different selection criteria. (PDF 399 kb)

Supplementary Fig. 3

Overlap of differentially expressed gene lists between different normalization methods. (PDF 29 kb)

Supplementary Fig. 4

Inter-site concordance of lists of differentially expressed genes based on fold-change, t-statistic, SAM, and random selection. (PDF 96 kb)

Supplementary Fig. 5

Cross-platform overlap of differentially expressed gene lists generated using different selection criteria. (PDF 1114 kb)

Supplementary Table 1

Summary of RNA Sample Information. (XLS 35 kb)

Supplementary Table 2

Cross-Platform Probe Sequence Mapping (5,112 commonly mapped rat genes). (XLS 1047 kb)

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Guo, L., Lobenhofer, E., Wang, C. et al. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol 24, 1162–1169 (2006). https://doi.org/10.1038/nbt1238

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