Correlation analysis of external RNA controls reveals its utility for assessment of microarray assay

National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA.
Analytical Biochemistry (Impact Factor: 2.22). 02/2009; 385(2):203-7. DOI: 10.1016/j.ab.2008.11.019
Source: PubMed


Quality control of a microarray experiment has become an important issue for both research and regulation. External RNA controls (ERCs), which can be either added to the total RNA level (tERCs) or introduced right before hybridization (cERCs), are designed and recommended by commercial microarray platforms for assessment of performance of a microarray experiment. However, the utility of ERCs has not been fully realized mainly due to the lack of sufficient data resources. The US Food and Drug Administration (FDA)-led community-wide Microarray Quality Control (MAQC) study generates a large amount of microarray data with implementation of ERCs across several commercial microarray platforms. The utility of ERCs in quality control by assessing the ERCs' concentration-response behavior was investigated in the MAQC study. In this work, an ERC-based correlation analysis was conducted to assess the quality of a microarray experiment. We found that the pairwise correlations of tERCs are sample independent, indicating that the array data obtained from different biological samples can be treated as technical replicates in analysis of tERCs. Consequently, the commonly used quality control method of applying correlation analysis on technical replicates can be adopted for assessing array performance based on different biological samples using tERCs. The proposed approach is sensitive to identifying outlying assays and is not dependent on the choice of normalization method.

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    • "Spiked controls can evaluate assay performance, at least for those steps of analysis after spiking occurs. Commercial RNA spikes of known sequence (developed by the External RNA Controls Consortium) can be added to each patient specimen either at the time that lysis buffer is added or later when RNA is being prepared for analysis.67–71 Their downstream measurement can detect interfering substances such as autofluorescence, heparin anticoagulant, hemoglobin protein or globin RNA, or residual phenol. "
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    Pharmacogenomics and Personalized Medicine 09/2011; 4(1):95-107. DOI:10.2147/PGPM.S14888
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    • "A significant observation from this work was the identification of outlier data at one participant's site using principal component analysis (PCA) of the external controls. More recent analysis of the various spike-in controls employed in the measurements for the MAQC project demonstrated promise that the spike-in controls were informative of "outlying" arrays, and that they exhibit behavior that is independent of the sample type [17]. "
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    BMC Research Notes 12/2010; 3(1):349. DOI:10.1186/1756-0500-3-349
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