ABC-DLS (Approximate Bayesian Computation with Deep Learning and Sequential monte carlo)

This software is a python pipeline where you can use simulated summary statistics to predict which underlying model can better explain the observed or real-world results (classification) and can guess which parameters can produce such results (parameter estimation) with the help of Approximate Bayesian Computation (ABC), Deep Learning (using Tensorflow Keras Backend) and Sequential Monte Carlo (SMC). The whole method is written in python and easy to read and can be accessed entirely through the command line, therefore knowing python is not needed. Although it will be helpful to know python and R as some of the packages here used are based on those languages.

Getting Started

To download the package, either click the download button in https://github.com/mayukhmondal/ABC-DLS or use:

git clone https://github.com/mayukhmondal/ABC-DLS
cd ABC-DLS

The codes are written in python3 (>=python3.6.9). This programme comes with several dependencies:

• numpy
• scikit-learn
• joblib
• pandas
• h5py
• rpy2
• r-essentials
• tzlocal
• tensorflow
• keras

The easiest way to install all of these dependencies is using conda. To install conda please visit anaconda. After installing conda (remember to install python 3), use:

conda install --file requirements.txt

or for last tested version

conda env update -f requirements.yml

or if you want to make a different environment:

conda create --name ABC-DLS --file requirements.txt  python=3
conda activate ABC-DLS

same but for last tested version. Use it in case you getting conflict between package versions.

conda env create -f requirements.yml --name ABC-DLS
conda activate ABC-DLS

Please wait a little bit as it can take a long time to install all the dependencies. After installing all the dependencies, you can just run either

python src/Run_Classification.py --help

or

python src/Run_ParamsEstimation.py --help

or

python src/Run_SMC.py --help

For Model selection, parameters estimation and parameter estimation using SMC, respectively. The first time you run, it will also try to install abc from r package manager (automatically). Please see examples/Examples.md for a detailed guide how to use the codes. Right now, this code is written and checked in the linux system. I can not guarantee it will work on other systems, but you are welcome to try.

Installation Issues

In case you try to install it to an already existed conda environment that already has R (r-base), it can conflict with the rpy2 when it tries to automatically download an abc package from R saying abc package does not exist. In that case, create a new environment.

Citation

Revisiting the out of Africa event with a deep learning approach

Francesco Montinaro, Vasili Pankratov, Burak Yelmen, Luca Pagani, Mayukh Mondal
The American Journal of Human Genetics; doi: https://doi.org/10.1016/j.ajhg.2021.09.006

Contact

The code is maintained by Dr. Mayukh Mondal. In case you need further assistance please contact mondal.mayukh@gmail.com