I'm assuming you have your data in two vectos:

alt.rna = seq(1, 10)
alt.dna = alt.rna + rnorm(10)

1. You need to model the linear function:

   m = lm(alt.dna ~ alt.rna)
   

2. Draw a plot:

   plot(alt.rna, alt.dna)
   

3. Add the modelled function to your plot:

   abline(m)
   

   

4. Create a detailed model of your function:

   model = summary(m)
   

5. To see distances of your points (residuals) to the modelled function:

   model$residuals
   
          1           2           3           4           5           6 
   -0.30676417  0.44577325  0.06274965 -0.88542591  1.15529305 -0.07406813 
   
          7           8           9          10 
   0.45455441 -1.68538423  0.59491190  0.23836017
   

6. Calculate standard deviation of the distances:

   sd(model$residuals)
   [1] 0.8082078​
   

I can only see the image when I edit you post. It seems you tried to drag and drop the image in. That doesn't work, you need to upload images somewhere and link to them.

Anyway, the deviation from linear is rather slight. Yes, the values are a bit higher in the RNA samples, but there's often a little bias in (A) what's expressed, (B) what's sequenced, and (C) what's aligned. Any and all of these could account for the slight difference you're seeing. You've also not told us exactly how you derived the allele frequencies. I hope these aren't from pooled datasets, since getting allele frequencies from pooled RNA samples would seem like a bad idea.