Question: Statistical method of deducing the probability of a ratio being 1:2 for pairs of numbers (read depth) in a set
0
gravatar for jomaco
5.0 years ago by
jomaco190
European Union
jomaco190 wrote:

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

 

ADD COMMENTlink modified 5.0 years ago by Brian Bushnell17k • written 5.0 years ago by jomaco190
0
gravatar for Brian Bushnell
5.0 years ago by
Walnut Creek, USA
Brian Bushnell17k wrote:

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.

ADD COMMENTlink modified 11 months ago by RamRS30k • written 5.0 years ago by Brian Bushnell17k
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