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.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
ERCC. Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics 6, 150 (2005).
ERCC. The External RNA Controls Consortium: a progress report. Nat. Methods 2, 731–734 (2005).
Hill, A.A. et al. Evaluation of normalization procedures for oligonucleotide array data based on spiked cRNA controls. Genome Biol 2, RESEARCH0055 (2001).
Rajagopalan, D. A comparison of statistical methods for analysis of high density oligonucleotide array data. Bioinformatics 19, 1469–1476 (2003).
Irizarry, R.A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).
Irizarry, R.A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).
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).
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).
Dabney, A.R. & Storey, J.D. A reanalysis of a published Affymetrix GeneChip control dataset. Genome Biol. 7, 401 (2006).
MAQC Consortium. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151–1161 (2006).
Guo, L. et al. Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat. Biotechnol. 24, 1162–1169 (2006).
Shippy, R. et al. Using RNA sample titrations to assess microarray platform performance and normalization techniques. Nat. Biotechnol. 24, 1123–1131 (2006).
“Guide to Probe Logarithmic Intensity Error (PLIER) Estimation”, Affymetrix Technical Note, http://www.affymetrix.com/support/technical/technotes/plier_technote.pdf
Microarray Suite User's Guide, Version 5.0, http://www.affymetrix.com/support/technical/manuals.affx
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).
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).
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).
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).
Tong, W. et al. Development of public toxicogenomics software for microarray data management and analysis. Mutat. Res. 549, 241–253 (2004).
Author information
Authors and Affiliations
Corresponding author
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)
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1038/nbt1237
This article is cited by
-
Development of a 16S rRNA gene-based microarray for the detection of marine bacterioplankton community
Acta Oceanologica Sinica (2017)
-
Microarray experiments and factors which affect their reliability
Biology Direct (2015)
-
Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures
Nature Communications (2014)
-
Quality assessment metrics for whole genome gene expression profiling of paraffin embedded samples
BMC Research Notes (2013)
-
mRNA enrichment protocols determine the quantification characteristics of external RNA spike-in controls in RNA-Seq studies
Science China Life Sciences (2013)