MiNEApy

This software can be used to perform minimal network enrichment analysis.

Vikash Pandey and Vassily Hatzimanikatis. Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data.DOI:https://doi.org/10.1371/journal.pcbi.1006760

Requirements

You will need to have Git-LFS in order to properly download some binary files:

git clone https://github.com/vpandey-om/mineapy.git /path/to/mineapy
cd /path/to/mineapy
git lfs install
git lfs pull

This module was developed in Python 3.7, and it is recommended to run Python 3.7 to run commercial solvers such as Gurobi and CPLEX.

This module requires COBRApy, pyTFA, as well as optlang to work properly. The installer should take care of that for you. You might also want to install a dedicated solver. GLPK, CPLEX and Gurobi are supported.

Setup

You will need to install conda and create an environment using python 3.7:

conda create -n py37 python=3.7
conda activate py37

Using pip

pip install mineapy

or from source

conda create -n py37 python=3.7
conda activate py37

# inside the py37 environment we can download from github and install.
git clone https://github.com/vpandey-om/mineapy.git /path/to/mineapy
cd /path/to/mineapy
pip install -e .

Quick start

Two tutorial files detail thoroughly normal usages of the mineapy package. They can be found at:

mineapy
└── tutorials
    ├── e_coli_core.py
    └── e_coli_gem.py

Usage

First, create your COBRApy model. Make sure to define the additional values required by pyTFA, as said in the "Models" page of the documentation.

If you already have a Matlab model with thermodynamic data, you might want to use pytfa.io.import_matlab_model. Otherwise, have a look at the COBRApy documentation, then add the required properties.

If you're using a specific solver, don't forget to tell COBRApy about it by setting the solver property of your model to the name of your solver. See the COBRApy documentation for more information about this.

Example script

Here is an example script :

import mineapy
from cobra.io import load_matlab_model
import pandas as pd
from mineapy.core.taskEnrich import TaskEnrichment
from mineapy.core.thermo_model import ThermoModel_WithoutInfo
from mineapy.core.rxnExp import ReactionExp

## load e_coli_core model
cobra_model= load_matlab_model('./models/e_coli_core.mat')
genes=[g.id for g in cobra_model.genes]

## Minea parameters
path_to_params = './input/task_enrichment_params.yaml'

## add condition- or context-specific expression data
context_df=pd.read_csv('./input/context.txt',sep='\t')
condition_df=pd.read_csv('./input/condition.txt',sep='\t')

## get genes that are regulated between different conditions
gene_reg={'gene_id':condition_df['geneid'].to_list(),'fold_change':condition_df['fold change'].to_list(),'up_cutoff':1.35,'down_cutoff':float(1/2.5)}
reg_analysis=ReactionExp(cobra_model,gene_reg=gene_reg)

## set cut off for example 15 % top and 15 % bottom in ranking
gene_exp={'gene_id':context_df['geneid'].to_list(),'exp_val':context_df['exp_val'].to_list(),'high_cutoff':0.15,'low_cutoff':0.15}
exp_analysis=ReactionExp(cobra_model,gene_exp=gene_exp)
params_rxns={'high_rxns':exp_analysis.high_rxns,'low_rxns':exp_analysis.low_rxns}

## Apply enrichment algorithms
task_enrich = TaskEnrichment(cobra_model,path_to_params,params_rxns)
task_enrich.run()

License

The software in this repository is put under an APACHE-2.0 licensing scheme - please see the LICENSE file for more details.