Name: hotnet2
Owner: raphael-group
Description: HotNet2 is an algorithm for finding significantly altered subnetworks in a large gene interaction network
Created: 2014-01-10 18:47:38.0
Updated: 2017-11-07 10:30:25.0
Pushed: 2018-01-01 22:06:34.0
Size: 2921
Language: Python
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HotNet2 identifies subnetworks of a protein-protein interaction network with more mutations (“heat”) than expected.
HotNet2 was developed by the Raphael research group at Brown University.
Latest tested version in parentheses:
Python (2.7.12)
a. NumPy (1.12.1)
b. SciPy (0.19.0)
c. NetworkX (1.11)
d. h5py (2.7.0)
gcc and gfortran (5.4.0)
We recommend using virtualenv to install the Python requirements. After installing virtualenv, you can install the Python requirements for HotNet2 as follows:
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
The C and Fortran extensions are not required, but will significantly speed up HotNet2. You can compile them as follows:
python hotnet2/setup_fortran.py build_src build_ext --inplace
python hotnet2/setup_c.py build_src build_ext --inplace
We recently updated HotNet2 to simplify usage. This update requires some updates to your scripts and data. Consult the previous README for comparison.
makeHeatFile.py to create a JSON file of gene heat scores (weights). These scores can be generated from mutation data or from several other formats. HotNet2.py requires a path to at least one heat file as input.makeNetworkFiles.py to generate the network files required for running HotNet2. These include the influence matrix of your input network, and permuted networks and their corresponding influence matrices. Each of these files are in HDF5 (.h5) format. HotNet2.py requires at least one network file and path to the permuted network files as input.See paper/paper_commands.sh for examples of using the makeHeatFile.py and makeNetworkFiles.py scripts.
After generating a heat file and the network files using the scripts above, use the HotNet2.py script to run HotNet2. The minimum arguments required for HotNet2.py are as follows:
python HotNet2.py -nf <network_file> -pnp <permuted_networks_path> -hf <heat_file> -o <output_directory>
See paper/paper_commands.sh for an example of using the HotNet2.py scripts with outputs of the makeHeatFile.py and makeNetworkFiles.py scripts.
The output of HotNet2.py consists of a directory containing the following:
{network_name}-{heat_name}/: For each (network, heat score) pair, HotNet2.py outputs a directory of results. The directory contains subdirectories starting with “delta” for each delta parameter tested, each of which contain the subnetworks and statistical signifciance associated with that delta parameter.consensus/: The consensus/ directory contains the consensus file across all networks and heat scores,scripts/consensus.py to generate the consensus file from the results of HotNet2 on a single network and heat score.scripts/createDendrogram.py to generate a dendrogram of strongly connected components in the HotNet2 exchanged heat graph.scripts/permuteNetwork.py to create a permuted edge list from an input network.scripts/createInfluenceMatrix.py to create a HotNet or HotNet2 influence matrix from an input network.See testing/README.md for testing instructions.
See paper/paper_commands.sh for a short but complete set of commands for reproducing the experiments in the HotNet2 paper.
We provide scripts to run an interactive web application to view the output of HotNet2.py, including the subnetworks in the consensus and individual runs. See viz/README.md and the wiki for additional instructions and details.
Please visit the HotNet Google Group to post questions and view discussions from other users about HotNet or HotNet2, or contact us through our research group's website.
If you use HotNet2 in your work, please cite
M.D.M. Leiserson*, F. Vandin*, H.T. Wu, J.R. Dobson, J.V. Eldridge, J.L. Thomas, A. Papoutsaki, Y. Kim, B. Niu, M. McLellan, M.S. Lawrence, A.G. Perez, D. Tamborero, Y. Cheng, G.A. Ryslik, N. Lopez-Bigas, G. Getz, L. Ding, and B.J. Raphael. (2014) Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations across Pathways and Protein Complexes. Nature Genetics 47, 106?114 (2015).
If you use HotNet in your work, please cite:
F. Vandin, E. Upfal, and B.J. Raphael. (2011) Algorithms for Detecting Significantly Mutated Pathways in Cancer. Journal of Computational Biology. 18(3):507-22.
F. Vandin, P. Clay, E. Upfal, and B. J. Raphael. Discovery of Mutated Subnetworks Associated with Clinical Data in Cancer. In Proc. Pacific Symposium on Biocomputing (PSB), 2012.
(* denotes equal contribution)