I'm reading [this excellent paper][1] which describes a methodology to standardize variant benchmarking process. They say that the normal binary classification form (i.e., TP, FP, FN, and statistics derived from these) are not simple for variant calls. So they go on to describe how they do this, in tabular form.

It's also worth noting that this is how they define sensitivity and specificity:

sensitivity (the ability to detect variants that are known to be present or “absence of false negatives”)
and specificity (the ability to correctly identify the absence of variants or “absence of false positives”)

They do not use specificity, opting instead for precision because "precision is often a more useful metric than specificity due to the very large proportion of true negative positions in the genome."

![contingency table][2]

I'm having a hard time understanding why FP is the way it is. I would've thought that all the n/a's in the first column (e,g,  "ref/var2" and ref/var3", "var1/var3" etc) would be FP as well.

It's also hard to decipher why the n/a's occur in the rest of the table? 
This may have something to do with their comment:

"Note that we have chosen not to include true negatives (or consequently specificity) in our standardized definitions. This is due to the challenge in defining the number of true negatives, particularly for indels or around complex variants."

Are the n/a's representing true negatives? It is all so confusing.

  [1]: https://www.biorxiv.org/content/biorxiv/early/2018/05/24/270157.full.pdf
  [2]: https://www.biorxiv.org/content/biorxiv/early/2018/02/23/270157/T1.medium.gif
> It's also hard to decipher why the n/a's occur in the rest of the table? 

I think it is because those scenarios can't happen. If you compar two individuals, and the Query has var1/var3 then there has to be at least a var2 in your comparison, otherwise you can't have var3.

If Query = GT:1/3 then the only scenarios for Truth are GT:1:2, in a vcf file.