I am currently undergoing my end of year research project which is testing if DNA can be found in a secondary transfer when multiple transfers have occurred, one of my replicates have a Ct value of 0, do I include this or find an average of the remaining replicates as when I have calculated out the fold change of my replicates it has a value of 20.29
You might need to provide more experimental design information.
Are you dealing with very low expected [template], such that Cqs are often 30+ (in which case "no amplification" genuinely implies "no target"), or are you dealing with fairly robust [template], i.e. Cqs of 20-30 (in which case "no amplification" means "freak weird event")?
If the former, the extent of quantitative info you can glean is going to be more limited anyway (stochastic template numbers will be inherently variable), and you should flag the well as "no amplification".
If the latter, you just ignore it.
Yeah all my cqs are above 30 so if it means no amplification do i include it in my average or no as when its been included the fold change is too high however if i dont include it in my average the fold change makes more sense compared to the rest of my result.
The issue here is that "no amplification" is indeed here telling you that you have no target in your well. Zero target.
You cannot use "fold change" on zero values, because the fold change from zero to anything at all is "infinite-fold".
The other thing to consider is whether fold change is an appropriate metric when dealing with such low levels of target: Cqs in the 30s represent countable numbers of molecules (probably less than ~20), and here you will have higher well-to-well variation just because with such small numbers you don't always get even partitioning. A sample with 10 targets per ul, pipetted three times (1ul each time) might give you 12, 8 and 6 molecules in your replicate wells, with corresponding high variation in Cq. One way to increase accuracy is to include more replicate wells: the mean of five or six highly variable Cqs is better than the mean of only three.
BUT.
If we assume "in-depth analysis of precise study methodology" isn't really the focus here, and instead just look for fairly cheap and cheerful solutions that "sort of" work, then I would suggest the following:
Establish, either by dilution curve or by just looking at your data, what Cq value likely represents a single template molecule (it will be around ~35, most likely).
You might, for example, find your data ranges from 28-34 but never gives values higher than 34.5.
You then clamp 34.5 as your value for "a single molecule was detected".
Now, when looking at your data, if you get a sample for which every replicate well is negative (no amplification) that sample is zero: not detected, no target.
For other samples where one out of the three wells is negative, but the others are, say, 33 and 34 (low but detectable), you just call the negative well "34.5", purely for the purposes of getting a mean that actually reflects the data. You are in essence pretending there was a single molecule in that well, because clearly there were SOME molecules in that sample (the other wells are positive). The answer might not be entirely accurate, but will be in the right ballpark (and when you're dealing with such low levels of targets, all your answers will be 'ballpark accuracy' at best, anyway).
Never use "0" as the Cq for that well, that's the exact opposite of what you want.
A Ct value of 0 means that the amplification was detected before the first cycle of PCR, which could be an indication of contamination or an extremely high starting concentration of the target DNA. It is important to confirm the validity of this result by checking the amplification curve and the melting curve. If the amplification curve shows a clear exponential increase and the melting curve shows a single peak, then the result could be valid.
In terms of delta Ct analysis, a Ct value of 0 cannot be used directly in the calculation because it represents an infinitely high starting concentration. One option is to assign a high Ct value, such as 40, to the sample with a Ct of 0 for the purpose of the calculation. Alternatively, you can exclude this sample from the analysis if you suspect it may be a result of contamination or technical issues. However, it is important to report this result and any potential issues in the methods and results sections of your research report.
These video playlists might be helpful to you:
https://www.youtube.com/playlist?list=PLzBiAPKi8vONSqjcweFWHMRy13y3484bE
https://www.youtube.com/playlist?list=PLzBiAPKi8vOMheKwb0mu1dcZRaeGUFSlw
https://www.youtube.com/playlist?list=PLzBiAPKi8vOPbE7Db9mKwloe34E8rXXEU
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