SJARACNe

SJARACNe is a scalable solution of ARACNe that dramatically improves the computational performance, especially on the memory usage to allow even researchers with modest computational power to generate networks from thousands of samples. The algorithm uses adaptive partitioning mutual information to calculate the correlation between all pairs of genes to reconstruct the regulatory network.

Download

git clone https://github.com/jyyulab/SJARACNe # Clone the repo

Prerequisites

• Python 3.6.1
  • numpy==1.14.2
  • scipy==1.0.1
  • pandas==0.22.0
  • cwltool==1.0.20190618201008
• cwlexec==0.2.2 (required for running on IBM LSF)

Installation

Using conda to create a virtual environment (recommended)

The recommended method of setting up the required Python environment and dependencies is to use the conda dependency manager:

$ conda create -n py36 python=3.6.1
$ source activate py36
$ conda install --file requirements.txt

Using pip

First install Python 3.6.1 and then use the following command to install SJARACNe and dependencies.

$ pip install SJARACNe

Install from source

$ git clone https://github.com/jyyulab/SJARACNe
$ cd SJARACNe
$ python setup.py build     # build SJARACNe binary
$ python setup.py install

Usage

usage: sjaracne [-h] {local,lsf} ...

SJARACNe is a scalable tool for gene network reverse engineering.

optional arguments:
  -h, --help   show this help message and exit

Subcommands:
  {local,lsf}  platforms
    local      run cwltool in a local workstation
    lsf        run cwlexec as in a IBM LSf cluster

sjaracne workflow is implemented with CWL. It supports multiple computing platforms. We have tested it locally using cwltool and on an IBM LSF cluster using cwlexec. For the convenience, a python wrapper is developed for you to choose computing platform using subcommand.

The local mode (sjaracne local) runs in parallel by default using cwltool's --parallel option. To run it in serial, use --serial option.

To use LSF mode, editing the LSF-specific configuration file SJARACNe/config/config_cwlexec.json to change the default queue and adjust memory reservation for each step is necessary. Consider increasing memory reservation for bootstrap step and consensus step if the dimension of your expression matrix file is large.

Inputs

The main input for SJARACNe is a tab-separated genes/protein by cells/samples expression matrix with the first two columns being ID and symbol. The second required input file is the list of significant genes/proteins IDs to be considered as hubs in the reconstructed network. An output directory is required for storing output files. Additional parameters (e.g., LSF queue) for running on different platforms are required. Those are available in the helping information of the corresponding subcommands, e.g., sjaracne lsf -h.

Outputs

The main output of SJARACNe is a network file, which is a tab delimited text file with the following columns: source, target, mutual information, Pearson and Spearman correlations coefficients, regression line slope and p-value. SJARACNe also outputs two meta information files: parameter_info_.txt and bootstrap_info_.txt, which stores SJARACNe input parameters and bootstrap parameters respectively.

Examples to create a transcription factor network

Running on a single machine (Linux/OSX)

sjaracne local -e ./test_data/inputs/BRCA100.exp -g ./test_data/inputs/tf.txt -n 2 -o ./test_data/outputs/cwl/cwltool/SJARACNE_out.final

Running on an IBM LSF cluster

sjaracne lsf -j ./SJARACNe/config/config_cwlexec.json -e ./test_data/inputs/BRCA100.exp -g ./test_data/inputs/tf.txt -n 2 -o ./test_data/outputs/cwl/cwltool/SJARACNE_out.final

Reference

Alireza Khatamian, Evan O. Paull, Andrea Califano* & Jiyang Yu*. SJARACNe: a scalable software tool for gene network reverse engineering from big data. Bioinformatics (2018). *Corresponding authors.