Introduction

EgoNet is implemented by Python and it is designed to detecting disease related subnetwork from a large biological network (PPI, metabolic network) combined with gene expression data.

Pre-installtalation

Python version 2.7 or later (http://www.python.org/)

Python packages:

• Biopython (http://biopython.org/wiki/Main_Page)
• NetworkX (http://networkx.github.io/)
• SciPy (http://www.scipy.org/)
• Numpy (http://www.numpy.org/)
• scikit-learn (http://scikit-learn.org/stable/)

Running EgoNet

Command-line usage:

python egonet.py -n <network_file> -g <gene_matrix_file> -o <output_file> [opts]

Options:

-m <int>	:method of classification or regression (default: class)
-t <float>	:percentage of top selected gene for searching (default: 1, no sort)
-s <float>	:score cutoff for printing selected subnetwork (default: 0.6)
-f <pickle>	:saved subnetwork python object used for visualization (default: subnetwork.py)
-r <txt>	:saved gene list ranked by two measuring methods (default: gene_rank.txt)
-h      	:produce this menu

Example:

python egonet.py -n sample_data/input/network.adjlist -g sample_data/input/gene_expression.txt -o TNBC.txt -f svm_net.pk

Example data are provide in the directory sample_data/

Input and Output File

1. network file is the adjacency list format
2. gene matrix file starts with gene name or entrize id as first column and expression values for other columns, the last row starts with "outcome" and labels for each sample
3. the output files contain the ranked subnetwork by predicting accuracy and ranked genes names using M-value.

M = msi

where m is total number of subnetwork contained gene, s is the score of each subnetwork and i is the importance of gene.

Visualize subnetwork

Selected subnetwork can be plotted using as followed:

python script/drawnet.py mark_gene diff_gene network_obj gene_matrix_file node

Example:

python script/drawnet.py sample_data/visualization/breastcancer.gene sample_data/visualizaiont/diffexpress.gene svm_net.pk sample_data/input/gene_expression.txt 675

Contact us

Questions, suggestions, comments, etc?

Author: Rendong Yang

Send email to [email protected]

Citation

Yang, Rendong, et al. "EgoNet: identification of human disease ego-network modules." BMC genomics 15.1 (2014): 314.