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Evaluation of external RNA controls for the assessment of microarray performance


External RNA controls (ERCs), although important for microarray assay performance assessment, have yet to be fully implemented in the research community. As part of the MicroArray Quality Control (MAQC) study, two types of ERCs were implemented and evaluated; one was added to the total RNA in the samples before amplification and labeling; the other was added to the copyRNAs (cRNAs) before hybridization. ERC concentration-response curves were used across multiple commercial microarray platforms to identify problematic assays and potential sources of variation in the analytical process. In addition, the behavior of different ERC types was investigated, resulting in several important observations, such as the sample-dependent attributes of performance and the potential of using these control RNAs in a combinatorial fashion. This multiplatform investigation of the behavior and utility of ERCs provides a basis for articulating specific recommendations for their future use in evaluating assay performance across multiple platforms.

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Figure 1: Overview of external RNA controls (ERCs) implemented in Affymetrix, Agilent, Applied Biosystems and GE Healthcare platforms.
Figure 2: Concentration-response curves for ERCs on the Agilent, Affymetrix and GE Healthcare microarray platforms.
Figure 3: Concentration-response linear regression results for the Agilent, Affymetrix and GE Healthcare microarray platforms.
Figure 4: Expected versus observed log10 ratio comparison for Agilent Two-Color ERC data.
Figure 5: Illustration of the sample-dependent behavior of tERC signal across the MAQC samples.
Figure 6: Alternative analysis using ERCs.

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  1. ERCC. Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics 6, 150 (2005).

  2. ERCC. The External RNA Controls Consortium: a progress report. Nat. Methods 2, 731–734 (2005).

  3. Hill, A.A. et al. Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls. Genome Biol 2, RESEARCH0055 (2001).

  4. Rajagopalan, D. A comparison of statistical methods for analysis of high density oligonucleotide array data. Bioinformatics 19, 1469–1476 (2003).

    Article  CAS  Google Scholar 

  5. Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).

    Article  Google Scholar 

  6. Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  Google Scholar 

  7. Freudenberg, J., Boriss, H. & Hasenclever, D. Comparison of preprocessing procedures for oligo-nucleotide micro-arrays by parametric bootstrap simulation of spike-in experiments. Methods Inf. Med. 43, 434–438 (2004).

    Article  CAS  Google Scholar 

  8. Choe, S.E., Boutros, M., Michelson, A.M., Church, G.M. & Halfon, M.S. Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset. Genome Biol. 6, R16 (2005).

    Article  Google Scholar 

  9. Dabney, A.R. & Storey, J.D. A reanalysis of a published Affymetrix GeneChip control dataset. Genome Biol. 7, 401 (2006).

    Article  Google Scholar 

  10. MAQC Consortium. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151–1161 (2006).

  11. Guo, L. et al. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat. Biotechnol. 24, 1162–1169 (2006).

    Article  CAS  Google Scholar 

  12. Shippy, R. et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat. Biotechnol. 24, 1123–1131 (2006).

    Article  CAS  Google Scholar 

  13. “Guide to Probe Logarithmic Intensity Error (PLIER) Estimation”, Affymetrix Technical Note,

  14. Microarray Suite User's Guide, Version 5.0,

  15. Wu, Z., Irizarry, R.A., Gentleman, R., Murillo, F.M. & Spencer, F. A model based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc. 99, 909–917 (2004).

    Article  Google Scholar 

  16. Li, C. & Wong, W. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl. Acad. Sci. USA 98, 31–36 (2001).

    Article  CAS  Google Scholar 

  17. Fang, H., Xie, Q., Boneva, R., Fostel, J., Perkins, R. & Tong, W. Gene expression profile exploration of a large dataset on chronic fatigue syndrome. Pharmacogenomics, 7, 429–440, (2006).

    Article  CAS  Google Scholar 

  18. Tong, W. et al. ArrayTrack–supporting toxicogenomic research at the US Food and Drug Administration National Center for Toxicological Research. Environ. Health Perspect. 111, 1819–1826 (2003).

    Article  CAS  Google Scholar 

  19. Tong, W. et al. Development of public toxicogenomics software for microarray data management and analysis. Mutat. Res. 549, 241–253 (2004).

    Article  CAS  Google Scholar 

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Correspondence to Weida Tong.

Supplementary information

Supplementary Fig. 1

Comparison of the correlation coefficients for each assay from the lines fit through the expected versus observed log10 ratios in Figure 4. (DOC 99 kb)

Supplementary Fig. 2

The tERC signal intensity across different RNA samples. (DOC 89 kb)

Supplementary Fig. 3

The effect of normalization methods on the tERC performance behavior across the same RNA samples illustrated in Supplementary Figure 2. (DOC 141 kb)

Supplementary Fig. 4

Observed log10 ratios for the AGL tERCs that are spiked in at intended 1:1 ratios in the Two-Color hybridization samples. (DOC 34 kb)

Supplementary Fig. 5

Correlation Assuming Percent Brain is Changed to mRNA Differences Between in the Samples. (DOC 110 kb)

Supplementary Fig. 6

The cERC signal intensity (y-axis) was compared across the four different RNA samples (A, B, C and D) for the ABI (top graph) and AFX (bottom graph) platforms. (DOC 34 kb)

Supplementary Fig. 7

The effect of normalization method on the sample independency of the cERC signal intensity (y-axis) for the AFX microarray platform. (DOC 66 kb)

Supplementary Fig. 8

Hierarchical cluster analysis for the One-Color AG1 platform based on either the tERC probes (A) or the biological probes (B). (DOC 157 kb)

Supplementary Fig. 9

Full Concentration-Response Curves for tERCs on the Agilent microarray platform. (DOC 267 kb)

Supplementary Table 1

Summary of cERC Concentration and tERC Molar Ratio Used for Plotting Concentration-Response Curves in Figure 1. (DOC 52 kb)

Supplementary Table 2

Summary of statistical results presented in Figure 3. (DOC 1945 kb)

Supplementary Table 3

Summary of statistical results presented in Figure 5. (DOC 39 kb)

Supplementary Table 4

Summary of tERC Concentration and Expected Two-Color Ratios for the AGL Platform. (DOC 39 kb)

Supplementary Methods (ZIP 2085 kb)

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Tong, W., Lucas, A., Shippy, R. et al. Evaluation of external RNA controls for the assessment of microarray performance. Nat Biotechnol 24, 1132–1139 (2006).

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