Jan Hellemans has a Master in Biotechnology (2000, UGent) and a PhD in Medical Genetics (2007, UGent). He is a postdoctoral fellow at the Ghent University Hospital and supervises a core facility for automated DNA-isolation, sequencing, genotyping and high-throughput real-time PCR. Jan is the designer and programmer of the qBase software that is being used by over 4000 researchers worldwide for qPCR data analysis. Together with Jo Vandesompele, he is active in the development of new concepts and algorithms for improved qPCR data analysis, and one of the founders of the RDML consortium. Jan Hellemans is author of several scientific articles in high-ranked journals, and active as a teacher in qPCR courses. Jan Hellemans and Jo Vandesompele founded Biogazelle in 2007 as s a Ghent University spin-off company.
More Accurate qPCR Data-Analysis through Robust Replicate Averaging, Missing Data Imputation and Inter-Run Calibration
Friday, 20 April 2012 at 14:45
Add to Calendar ▼2012-04-20 14:45:002012-04-20 15:45:00Europe/LondonMore Accurate qPCR Data-Analysis through Robust Replicate Averaging, Missing Data Imputation and Inter-Run CalibrationSELECTBIOenquiries@selectbiosciences.com
The basic principles of qPCR based relative quantification have been around for almost a decade. Since the publication of the delta-delta-Ct many improvements have been developed and adopted by both the research community and industry. Here, we will show that there is room for further improvements and present our insights on more accurate qPCR data analysis. Inter-run variation. Theoretically, spreading measurements for a single assay across multiple runs is part of a suboptimal experimental design. With a few basic measures, the impact of inter-run variation can be minimized. Several approaches to subsequent inter-run calibration have been described. We will describe the benefits, drawbacks, limitations and pitfalls of these procedures. Imputation. The gold standard for normalization of qPCR expression data is normalization against multiple validated reference genes. Larger experiments pose an increased risk of missing data for any of these reference genes, resulting in the inability to analyze the affected sample and to evaluate the global stability of the expression of the selected reference genes. We will describe a proper imputation procedure to recover the data for these samples. Robust mean. PCR replicates are a common way to improve the accuracy of the obtained results. We will show that the median Cq value is a more robust measure than the typical arithmetic mean. Median results are very similar to means with outlier removal (Grubbs), but much easier to implement. We will also present robust measures for replicate variability.
Add to Calendar ▼2012-04-19 00:00:002012-04-20 00:00:00Europe/LondonAdvances in qPCRSELECTBIOenquiries@selectbiosciences.com
Add to Calendar ▼2012-04-20 14:45:002012-04-20 15:45:00Europe/LondonMore Accurate qPCR Data-Analysis through Robust Replicate Averaging, Missing Data Imputation and Inter-Run CalibrationSELECTBIOenquiries@selectbiosciences.com
