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qPCR Data Mining – How to Get the Most Out of Your qPCR Experiment

Held in conjunction with Genomics Research Asia

15 Nov 2012 - 16 Nov 2012, at 09:00-17:00 in Daejeon, South Korea


The objective of this course is to provide an understanding of data processing and statistical methods applicable for analysis of data obtained by real-time PCR. This course is based on seminars and computer-based exercises.

Learning Objectives

After completed course students will know:

  • How to estimate nucleic acid levels in test samples relative to standard samples and the precision of the estimate.
  • How to determine the level of detection (LOD) of a qPCR based analytical procedure.
  • How to compare the expression of targeted genes in two or more samples, and assess if the measured difference is statistically significant.
  • How to identify the most confounding experimental steps, whose performance is most important to improve
  • How to design qPCR experiment for optimum cost-performance
  • How to estimate the number of subjects needed in a study in order to be able to detect a certain treatment effect against the background of confounding variation.
  • How to select optimum normalization strategy.
  • How to identify patterns among expressed genes in profiling studies
  • How to separate sample classes in multimarker expression profiling studies
  • The principles of multimarker molecular diagnostics

Topics and Course Organization

  • Introduction to qPCR theory, ?Cq and ??Cq
  • Absolute quantification, qPCR standard curve, Reverse calibration, Limit of detection
  • Experimental design, Noise contributions to RT-qPCR analysis (nested ANOVA), cost-performance optimization of experiments
  • Sample size estimations (Power testing)
  • Selecting reference genes (geNorm, Normfinder)
  • Relative quantification, qPCR data pre-processing, Outlier detection. Comparison of groups (parametric and non-parametric methods)
  • Expression profiling, missing data treatment, scaling of data, Un-supervised clustering of genes and samples (hierarchical clustering, self-organized maps, Principal Component Analysis), Supervised clustering of samples
  • Exercises

Mikael Kubista

Mikael Kubista, Professor/Founder, TATAA Biocenter AB