Evaluation of DNA microarray results with quantitative gene expression platforms.

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.

Quality-controlled measurement methods for quantification of variations in transcript abundance in whole blood samples from healthy volunteers.

BACKGROUND: Transcript abundance (TA) measurement in whole blood frequently is conducted to identify potential biomarkers for disease risk and to predict or monitor drug response. Potential biomarkers discovered in this way must be validated by quantitative technology. In this study we assessed the use of standardized reverse transcription PCR (StaRT-PCR) to validate potential biomarkers discovered through whole blood TA profiling. METHODS: For each of 15 healthy volunteers, 6 blood samples were obtained, including 3 samples at each of 2 separate visits. Total variation in TA for each gene was partitioned into replicate, sample, visit, study participant, and residual components. RESULTS: Variation originating from technical processing was <5% of total combined variation and was primarily preanalytical. Interindividual biological sample variation was larger than technical variation. For 12 of 19 tests, the distribution of measured values was gaussian (Shapiro-Wilks test). CONCLUSION: For control or diseased population groups with variation rates as low as those observed in this control group, 17 individuals per group would be required to detect 1 SD change with 80% power with a 2-sided alpha = 0.05 statistical test for mean differences.