AGRN: Accurate Gene Regulatory Network Inference using Collective Machine Learning Methods

Table of Content

• Setup
• Dataset
• Prerequisites
• Download and install code
• Demo
• Run with Docker

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Dataset

The dataset can be found in the dataset directory. Both DREAM4 and DREAM5 datasets are inside the AGRN_tool.zip tool

Prerequisites

You would need to install the following software before replicating this framework in your local or server machine.

Python version 3.7.4

Poetry version 1.1.12

You can install poetry by running the following command:

curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -

To configure your current shell run `source $HOME/.poetry/env`

Download and install code

• Retrieve the code

wget http://cs.uno.edu/~tamjid/Software/AGRN/AGRN_tool.zip
unzip AGRN_tool.zip

Demo

To run the program, first, set the input path in the input.txt file. Here is a sample input file form DREAM5 E-coli Dataset.

./Dataset/DREAM5/training data/Network 1 - in silico/net1_expression_data.tsv
./Dataset/DREAM5/test data/DREAM5_NetworkInference_GoldStandard_Network1 - in silico.tsv
./Dataset/DREAM5/training data/Network 1 - in silico/net1_transcription_factors.tsv
./Dataset/DREAM5/training data/Network 1 - in silico/net1_gene_ids.tsv

Then, run following python command from the root directory.

cd AGRN_tool
poetry install
poetry run python AGRN.py

• Finally, check output folder for results. The output directory contains importance scores from ETR, SVR and RFR in csv files. The OutputResults.txt file shows the results in AUROC and AUPR.

Run with Docker

• Build the docker image from Dockerfile.

export UID=$(id -u)
export GID=$(id -g)
docker build --build-arg USER=$USER \
             --build-arg UID=$UID \
             --build-arg GID=$GID \
             --build-arg PW=asdf \
             -t agrn\
             -f Dockerfile.txt\
             .

• Mount the Output direcotry in the Docker Container and run it.

docker run -ti  -v /$(pwd)/Output:/home/$USER/Output agrn:latest

• Then, run following python command from the root directory.

source $HOME/.poetry/env
poetry run python AGRN.py

• Finally, check output folder for results. The output should be available in both host and docker. The output directory contains importance scores from ETR, SVR and RFR in csv files along with a OutputResults.txt file that shows the results in AUROC and AUPR.

Authors

Duaa Mohammad Alawad, Md Wasi Ul Kabir, Md Tamjidul Hoque. For any issue please contact: Md Tamjidul Hoque, thoque@uno.edu