When we do relative quantification of test gene with a house keeping gene, is it necessary to include PCR efficiency? If yes, Could someone provide reference?
Does MIQE guidelines made it mandatory to include PCR efficiency during calculating relative quantification?
I have no reference about this. But the efficiencies of both qPCR (housekeeping gene and test gene) are important. A reduce efficiency of one qPCR will modify your relative amount of tested gene due to shift one data at some values.
If you are at an efficiency of 2, in your range of value, your qPCR follow the theoretical model. If your are bellow 2 , your qPCR do not completly follow the model and the efficiency is at 100%.
I have no reference about this. But the efficiencies of both qPCR (housekeeping gene and test gene) are important. A reduce efficiency of one qPCR will modify your relative amount of tested gene due to shift one data at some values.
If you are at an efficiency of 2, in your range of value, your qPCR follow the theoretical model. If your are bellow 2 , your qPCR do not completly follow the model and the efficiency is at 100%.
Easy answer would be "if your efficiency is low enough to significantly affect your data, don't use that data, redesign your primers instead".
Practically, most qPCRs should be around 95-100% efficient, and over this range marginal differences in efficiency do not generally make a huge difference to relative values.
However, it is fortunately incredibly easy to factor efficiency into the calculation, so if you have measured it, you might as well.
When converting a series of Cq values to relative quantities, simply use this formula:
Relative quantitycurrent sample = efficiency(lowest measured sample Cq - current sample Cq)
Efficiency here represents the doubling each cycle, so 100% efficiency = 2.0, while 95% efficiency = 1.95.
I agree: the efficiency *should* be close to 2 (100%). If not, the whole quantification is questionable; there are a number of reasons;
if a primer pair reaches only low efficiency,
- it is not certain that it will perform similarily in different samples*
- it is not clear if the efficiency is constant during the whole PCR (what is a critical assumption for the calculations)
- the actual efficiency is hard to estimate with sufficient precision. A biased or wrong estimation will do even more harm to the results
Only robustly working solution: If you have doubts that the amplification efficiency is less than optimal, use different primers.
@ John: your solution only works when the data is not normalized to a loading control (reference gene). When both, target gene and reference gene have a different efficiency, the calculation is more difficult. The most difficult aspect, however, is to correctly include/propagate the uncertainty of the efficiency estimates to the final results. This may become relevant since it may be of the size of the technical variance (and even more). To give an example: you may deterimine a triplicate with mean ct of 25 and a ci of 0.2. Given the efficiency is estimated with 1.8 with ci of 0.05, the "efficiency corrected" extreme ct values (giving the ct that was obtained if the efficiency was 2.0 instead) are log2(1.9^25.2) = 23.3 and log2(1.8^24.8) = 21.03, indicating a ci 1.15 cycles. Thus, the uncertainty introduced by the efficiency estimation is far bigger than the uncertainty of the technical replicates. If this is the uncertainty, any detection of less than four-fold differences using triplicates will be impossible - even if there is no biological variability!
* in principle, the efficiency might be estimated from the slope of the log-linear part of the individual amplification curves. But this estimate is very imprecise (only few points can be used) and likely biased (due to background-trends and the decision which points of the curve to use). Usually (though not always) such etimates make the results worst, and it turned out better only to use these slopes to identify "outliers" that will be excluded from further analysis rather than correcting their results for the differing efficiency. The sample-wise determination of the apparent efficiency by dilution series is also possible but really costly, so often not feasable.
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