Statistical method of deducing the probability of a ratio being 1:2 for pairs of numbers (read depth) in a set

By looking at the numbers below (average depth of DNA sequencing read coverage across transcript regions) I am trying to decide whether for each line V1 and V2 are equal or whether V1 is half of V2 i.e. it looks like the first 12 lines potentially have a ratio of 1:2 (V1:V2) and the rest probably have a ratio closer to 1:1 (V1:V2).

Is there a statistical method of deducing the probability of the ratio being 1:2 (V1:V2)?

I am a novice statistician and have only used methods to find the whether two sets of data have means which are significantly different from each other e.g. Student's t-test, I am unsure how to compare individual numbers, perhaps taking into account the differences in the numbers in the dataset as a whole.

 

 

    V1              V2  

    26.4048096192   40.5971943888  

    42.7286338116   67.3218964442  

    16.6910523354   28.449634215  

    25.6696629213   39.7415730337  

    22.8902439024   35.4198606272  

    23.4578783151   42.8853354134  

    30.844304522    47.2364052662  

    21.3841059603   45.9911699779  

    26.9329608939   53.3240223464  

    25.6696629213   39.7415730337  

    22.8902439024   35.4198606272  

    23.4578783151   42.8853354134  

    48.4988523336   42.5401683244  

    66.9087759815   56.2771362587  

    30.9506517691   44.8621973929  

    41.0501567398   36.207523511  

    14.7519747235   11.4992101106  

    98.5691964286   86.9235491071  

    112.394004283   109.324411135  

    77.6472694718   70.7502238138  

    16.791973792    15.1638001638  

    47.6559691913   40.216944801  

    52.3489795918   48.362244898  

    83.0362537764   78.9007337074  

    47.7375565611   42.4809954751  

    85.6609654124   78.0877993158  

    85.1274038462   90.8990384615  

    153.882743363   126.502212389  

    99.50558109     93.8778726198  

    66.8800773694   58.5222437137  

    30.3466026081   29.2868908717  

    55.5277078086   52.7877833753  

    81.8555408971   74.9525065963  

    86.95684803     82.0356472795  

    85.0009587728   81.0872483221  

    203.495197439   135.008537887  

    70.7687908497   74.9027777778  

    87.9617065557   82.0097814776  

    84.094017094    83.5337132004  

    30.3687707641   45.0897009967  

    93.0515653775   90.2780847145  

    80.6575875486   79.2879377432  

    21.380224261    19.6819571865  

    40.7149700599   40.7023952096  

    103.873167982   94.3038331454  

    44.9216677195   44.0650663298  

    22.7315377932   19.5851433536  

    31.1595383571   25.9450101833  

    144.497706422   142.811926606  

    80.0854621458   70.4226660474  

    80.4943127962   72.2900473934  

    18.4689213894   20.7294332724  

    123.254526092   126.424920128  

    26.4048096192   40.5971943888  

    42.7286338116   67.3218964442  

    89.939923539    86.7706171491  

    74.0379020287   67.8980702622  

    84.1919866444   74.8113522538

 

It's important to realize that there is a reference-bias when mapping reads, particularly with a mapping program that has low tolerance for errors. Baited exon-capture data also causes such a bias. As such, when looking at ratios, a 100/140 split might have a near-zero chance of occurring when flipping coins, but... it would be a pretty strong indication of heterozygousity in sequencing.

In short, due to bias (which cannot be effectively calculated), theoretical techniques that assume unbiased random distributions will not give you a very useful answer. It's better to determine empirically, for a given type of data, what kind of ratios you see in the mapped reads when something has a 2:1 or 1:1 or 0:1 genotype.