Functional failure in essential pathways or proteins leads to the loss of biological function. Network resilience can quantify the interactome’s robustness to such random breakdowns. As described in (Zitnik et al., 2019), ResNet computes and plots the graph resilience for any gene/protein or non-biological network, also allowing users to remove nodes of their choice and view the resulting fragmented network. ResNet is written in Python.
Import ResNet.py as a module within the Python interpreter of your choice and run the functions. For convenience, make sure the dependent packages and ResNet.py are on the same path. For example, install the dependencies (including ResNet) within the same Conda environment when using Conda or in the same folder when using JupyterLab.
from ResNet import *
Required packages include-
pandas, os, statistics, numpy, plotly, networkx, random, math, scipy, timeit
(Code compatible with Python version 3.8.8. Higher or lower Python versions may cause discrepencies)
Input file = Network file The networks are considered undirected and binary.
Output files = Statistics files; Interactive Resilience plots
input: (network_file= .txt file with input network in with first two columns with node-pairs per connection) ;
(op_stat_filename= prefix for the output .txt file);
(reps= No. of iterations/ repetitions to perform serial node removal; default=5);
(bin= 'yes'/'no', default='yes'; choice of whether to bin the failure rates in 100 values to reduce run-time.
If you don't want to bin- The count of failure rates will be equal to total Nodes (N) in the network (for
e.g. f = [0/N, 1/N, 2/N, .... N/N])
output: (Output.txt file with Failure rates coresponding to the Entropy values)
input: (op_stat_filename= prefix for the output .html plots- SAME as the one in 'modified_shannon_div');
(reps= No. of iterations/ repetitions used in 'modified_shannon_div'; default=5);
output: (Plot1: Entropy (H) vs failure rates (f) ranging [0,1]);
(Plot2: # Components vs F), Both plots saved as .html to current directory
input: (network_file= .txt file with input network in with first two columns with node-pairs per connection);
(removed_nodes_file= List of nodes to be removed in a .txt file, either single or all at once);
(op_filename= prefix for the output .txt file);
output: (.txt file with size and proteins in fragmented network components)
If you found our tool useful in your analysis, we ask that you cite us as:
(Citation to be displayed soon as our publication is in the reviewing process)