Analyzing data from different qPCR experiments that are spread over time in qbase+

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You need to do inter-run calibration if you want to compare samples from different runs e.g.:

  • when it is not possible to get all samples for the same gene on the same plate
  • when you do additional runs weeks or months after your initial experiment

Of course there is a lot of variability between runs on a qPCR instrument:

  • thermal block is not always heating uniformously
  • quality of the lamp, the filters and the detector decreases over time
  • data analysis settings on the qPCR instrument (baseline correction and threshold) can be slightly different
  • efficiency of reagents (polymerase, fluorophores) is variable
  • optical properties of the plastic plates vary
Fortunately, inter-run calibration allows you to eliminate most of this variability.

In this experiment we will analyze the data from the gene expression experiment (see Analyzing gene expression data in qbase+) together with data from 2 runs (Run4 and Run5) that were done weeks after the initial gene expression experiment.

Because the data comes from two different experiments spread over time, we have included three inter-run calibrators on the plates: Sample01, Sample02 and Sample03.

Handicon.png Inter-Run Calibrators (IRC): samples that are repeated in each run when data from different experiments is combined

The principle of the IRCs is very similar to that of the reference genes:
In theory, the IRCs should have the same NRQ in each run.
In practice, the difference in NRQ between two runs is a measure of the inter-run variation and can be used to adjust the NRQs to remove the inter-run variation.

A detailed description on how to process technical replicates in qbase+ can be found in our video tutorial [1].

Creating a new Experiment

Loading the data

Analyzing the data

In Analyzing gene expression data in qbase+ we have already checked the stability of the reference genes (see Normalization section).
We determined that Flexible did not show stable expression.

Remember that for each target the variability of the normalized expression levels of the IRCs between different runs will be used to adjust the other normalized expression levels of that target gene. The adjustment is done by amplifying the normalized expression levels with a calibration factor that is calculated based on the normalized expression levels of the IRCs.
Since variability between runs is the same for each IRC, you expect that all IRCs measure the variability between the runs to the same extent, hence leading to similar calibration factors.