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ABC inference from empirical data

At this point, you have a folder containing summary statistics and observed data to perform ABC inference. As explained in our home page, we performed the ABC inference using ABCreg. However, you can used other ABC software to perform the inference.

We link ABCreg with Julia to perform ABC inference. If you are going to use ABCreg to make inferences from our software directly, please cite the publication. Remember you need to install ABCreg before continue. Please check home page to install ABCreg.

It is possible to perform the inference through Julia. We set the tolerance value such that 1000 acceptances were recorded for the inference

Analytical.ABCreg(analysis_folder="analysis/",S=size(adap.dac,1),tol=0.025,abcreg="/home/jmurga/ABCreg/src/reg");

The function will output one file per bootstrapped replicas containing the posteriors distributions. The posterior distributions contains five columns corresponding to :

  • α weak: Contribution of weak selecction to $\alpha$
  • α strong: Contribution of strong selecction to $\alpha$
  • α: Adaptation rate
  • γ: Negative selection coefficient
  • β: DFE shape parameter

We used R to estimate the Maximum-A-Posteriori (MAP) from posterior distributions following ABCreg examples. We linked Julia and R internally. The module contains functions to perform the estimations without quit the Julia session.

If you will perform MAP estimates and plot using our module, be sure you have installed R and the following packages: ggplot2 and data.table, locfit. 

tgpmap = Analytical.plot_map(analysis_folder="analysis/");
DataFrames.describe(tgpmap)
 Row │ variable  mean          min           median        max          nmissing  eltype   
     │ Symbol    Float64       Float64       Float64       Float64      Int64     DataType 
─────┼─────────────────────────────────────────────────────────────────────────────────────
   1 │ aw           0.108927     0.010857       0.108796      0.19754          0  Float64
   2 │ as           0.0506607   -0.00750128     0.0514826     0.134143         0  Float64
   3 │ a            0.152842     0.0962341      0.149083      0.233131         0  Float64
   4 │ gamNeg    1184.81       512.458       1277.47       1903.12             0  Float64
   5 │ shape        0.142934     0.128369       0.14189       0.167394         0  Float64

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