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Evaluation of external RNA controls for the assessment of microarray performance

Nature Biotechnology volume 24, pages 1132–1139 (2006)Cite this article

Abstract

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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Authors and Affiliations

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

    Weida Tong, Xiaohui Fan, Lei Guo & Leming Shi

  2. Agilent Technologies, Inc., 5301 Stevens Creek Blvd., Santa Clara, 95051, California, USA

    Anne Bergstrom Lucas, Patrick J Collins & Svetlana Shchegrova

  3. GE Healthcare, 7700 S. River Pkwy., Suite #2603, Tempe, 85284, Arizona, USA

    Richard Shippy

  4. Pharmaceutical Informatics Institute, Zhejiang University, Hangzhou, 310027, China

    Xiaohui Fan

  5. Z-Tech Corporation, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Rd., Jefferson, 72079, Arkansas, USA

    Hong Fang & Huixiao Hong

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

    Michael S Orr & Sue-Jane Wang

  7. SAS Institute Inc., SAS Campus Drive, Cary, 27513, North Carolina, USA

    Tzu-Ming Chu, Wenjun Bao & Russell D Wolfinger

  8. Affymetrix, Inc., 3420 Central Expressway, Santa Clara, 95051, California, USA

    Xu Guo & Janet A Warrington

  9. Applied Biosystems, 850 Lincoln Centre Dr., Foster City, 94404, California, USA

    Yongming Andrew Sun

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Corresponding author

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). https://doi.org/10.1038/nbt1237

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