The underlying answer to the question is have you experimentally controlled for in vitro artefacts? Thats taking a cloned virus and performing the same RT-PCR. If not thats makes things very difficult. If there isn't a cloned virus thats a problem. You might need to ask your wet-lab colleagues what old school "cloning a virus" means, or Biology SE will explain this. The reason this is important is given below.
The first step to the question is simply to plot a histogram against copy number. What you will find is a heavily skewed distribution where the copy number of one variant will represent >40% (at a wild guess) of all the viral copy number. Moreover, a handful (less than 100) of variants will comprise 90% of all the copy number. Once that is established it is much easier to assess what is a true variant. What I'm saying is the difference is one log minimum between what you think you are looking at and what is there.
The sources of artefact:
- RT-PCR artefacts, particularly if there is an RT step: Taq polymerase alone has an error rate of 1 in 1000 and RT has a very high error rate, worse it's the first step.
What DNA polymerase was used?
The key issue is separating the RT-PCR artefacts from naturally occurring RNA polymerase errors in situ is not possible without a control. If a frequency analysis cut-off strategy is used it needs to be an ultra-conservative threshold, i.e. I mean ridiculously high and this means lot of variants are going to be dumped.
Why? Again it's back to the problem whether the variants were generated in vitro, i.e. artefacts, or in situ, i.e. resulting from RNA polymerase. Generating a cut-off within a skewed distribution is not trivial per se but extremely difficult when the ratio between artefact and genuine variant has not been controlled.
There appears some sort of control. Maximising the threshold: thats a function of the control, so the control needs to accommodate a model of the different numbers of input variables; it will not be a linear function. Basically what input will give me the best representation of non-artifactual variants, that is what the control has to find out. Thus, for every input, you want a control.
Again, if the control is lacking, in any context, then the rule of thumb is to be ultra-conservative, not trying to maximise the number of variants, but minimise them, particularly if the control isn't perfect. Most virological reviewers raise concern with the numbers been reported here on the information given (obviously we don't know the virus).
- NGS sequencing is another source of artefact - I should have mentioned there are some famous disasters here. I'm sure you NGS is really good, but a small number of investigators have deposited a disproportionate number of artefact ridden sequences - that everyone knows about.
from comments (again)
A control of 4 is tiny in data science especially a GLM approach. However, other studies would have been performed - possibly lots of them if this was e.g. HIV - and controls would have been run. There are two major strategies (really 3) for biologically cloning a virus. Really you'll need 100+ controls, that may not be feasible but as many within 4 to 100 as are available. GLM would be a good approach IMO. It would need stating that this does not truly control the rate of artefacts, but it's much closer.
Final comments ... don't want to assess thresholds.
I gave this question some thought and I think it would work via machine learning (what I specialise in). However, it may be difficult to find appropriate published controls and/or augmentation. Personally, I'm satisfied this would work if done correctly, but implementation is fairly advance ML. Given the amount of time I've spent on thinking this question through I wouldn't use an alternative approach, because I think it would be valid. Augmentation BTW is a generic term in machine learning and deep learning to increase the size of the training set.