Learning from microarray interlaboratory studies: measures of precision for gene expression

<p>Abstract</p> <p>Background</p> <p>The ability to demonstrate the reproducibility of gene expression microarray results is a critical consideration for the use of microarray technology in clinical applications. While studies have asserted that microarray data can be "highly reproducible" under given conditions, there is little ability to quantitatively compare amongst the various metrics and terminology used to characterize and express measurement performance. Use of standardized conceptual tools can greatly facilitate communication among the user, developer, and regulator stakeholders of the microarray community. While shaped by less highly multiplexed systems, measurement science (metrology) is devoted to establishing a coherent and internationally recognized vocabulary and quantitative practice for the characterization of measurement processes.</p> <p>Results</p> <p>The two independent aspects of the metrological concept of "accuracy" are "trueness" (closeness of a measurement to an accepted reference value) and "precision" (the closeness of measurement results to each other). A carefully designed collaborative study enables estimation of a variety of gene expression measurement precision metrics: repeatability, several flavors of intermediate precision, and reproducibility. The three 2004 Expression Analysis Pilot Proficiency Test collaborative studies, each with 13 to 16 participants, provide triplicate microarray measurements on each of two reference RNA pools. Using and modestly extending the consensus ISO 5725 documentary standard, we evaluate the metrological precision figures of merit for individual microarray signal measurement, building from calculations appropriate to single measurement processes, such as technical replicate expression values for individual probes on a microarray, to the estimation and display of precision functions representing all of the probes in a given platform.</p> <p>Conclusion</p> <p>With only modest extensions, the established metrological framework can be fruitfully used to characterize the measurement performance of microarray and other highly multiplexed systems. Precision functions, summarizing routine precision metrics estimated from appropriately repeated measurements of one or more reference materials as functions of signal level, are demonstrated and merit further development for characterizing measurement platforms, monitoring changes in measurement system performance, and comparing performance among laboratories or analysts.</p

Exploring the use of internal and externalcontrols for assessing microarray technical performance

<p>Abstract</p> <p>Background</p> <p>The maturing of gene expression microarray technology and interest in the use of microarray-based applications for clinical and diagnostic applications calls for quantitative measures of quality. This manuscript presents a retrospective study characterizing several approaches to assess technical performance of microarray data measured on the Affymetrix GeneChip platform, including whole-array metrics and information from a standard mixture of external spike-in and endogenous internal controls. Spike-in controls were found to carry the same information about technical performance as whole-array metrics and endogenous "housekeeping" genes. These results support the use of spike-in controls as general tools for performance assessment across time, experimenters and array batches, suggesting that they have potential for comparison of microarray data generated across species using different technologies.</p> <p>Results</p> <p>A layered PCA modeling methodology that uses data from a number of classes of controls (spike-in hybridization, spike-in polyA+, internal RNA degradation, endogenous or "housekeeping genes") was used for the assessment of microarray data quality. The controls provide information on multiple stages of the experimental protocol (e.g., hybridization, RNA amplification). External spike-in, hybridization and RNA labeling controls provide information related to both assay and hybridization performance whereas internal endogenous controls provide quality information on the biological sample. We find that the variance of the data generated from the external and internal controls carries critical information about technical performance; the PCA dissection of this variance is consistent with whole-array quality assessment based on a number of quality assurance/quality control (QA/QC) metrics.</p> <p>Conclusions</p> <p>These results provide support for the use of both external and internal RNA control data to assess the technical quality of microarray experiments. The observed consistency amongst the information carried by internal and external controls and whole-array quality measures offers promise for rationally-designed control standards for routine performance monitoring of multiplexed measurement platforms.</p

in the Analysis of Foods and Dietary Supplements: A Practical Guide

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Evaluating Droplet Digital Polymerase Chain Reaction for the Quantification of Human Genomic DNA: Lifting the Traceability Fog

Digital polymerase chain reaction (dPCR) end point platforms directly estimate the number of DNA target copies per reaction partition, λ, where the partitions are fixed-location chambers (cdPCR) or aqueous droplets floating in oil (ddPCR). For use in the certification of target concentration in primary calibrant certified reference materials (CRMs), both λ and the partition volume, <i>V</i>, must be metrologically traceable to some accessible reference system, ideally, the International System of Units (SI). The fixed spatial distribution of cdPCR chambers enables real-time monitoring of PCR amplification. Analysis of the resulting reaction curves enables validation of the critical dPCR assumptions that are essential for establishing the SI traceability of λ. We know of no direct method for validating these assumptions for ddPCR platforms. The manufacturers of the cdPCR and ddPCR systems available to us do not provide traceable partition volume specifications. Our colleagues at the National Institute of Standards and Technology (NIST) have developed a reliable method for determining ddPCR droplet volume and have demonstrated that different ddPCR reagents yield droplets of somewhat different size. Thus, neither dPCR platform by itself provides metrologically traceable estimates of target concentration. We show here that evaluating split samples with both cdPCR and ddPCR platforms can transfer the λ traceability characteristics of a cdPCR assay to its ddPCR analogue, establishing fully traceable ddPCR estimates of CRM target concentration

Evaluating Droplet Digital Polymerase Chain Reaction for the Quantification of Human Genomic DNA: Lifting the Traceability Fog

Digital polymerase chain reaction (dPCR) end point platforms directly estimate the number of DNA target copies per reaction partition, λ, where the partitions are fixed-location chambers (cdPCR) or aqueous droplets floating in oil (ddPCR). For use in the certification of target concentration in primary calibrant certified reference materials (CRMs), both λ and the partition volume, <i>V</i>, must be metrologically traceable to some accessible reference system, ideally, the International System of Units (SI). The fixed spatial distribution of cdPCR chambers enables real-time monitoring of PCR amplification. Analysis of the resulting reaction curves enables validation of the critical dPCR assumptions that are essential for establishing the SI traceability of λ. We know of no direct method for validating these assumptions for ddPCR platforms. The manufacturers of the cdPCR and ddPCR systems available to us do not provide traceable partition volume specifications. Our colleagues at the National Institute of Standards and Technology (NIST) have developed a reliable method for determining ddPCR droplet volume and have demonstrated that different ddPCR reagents yield droplets of somewhat different size. Thus, neither dPCR platform by itself provides metrologically traceable estimates of target concentration. We show here that evaluating split samples with both cdPCR and ddPCR platforms can transfer the λ traceability characteristics of a cdPCR assay to its ddPCR analogue, establishing fully traceable ddPCR estimates of CRM target concentration