Name: hierarchical-hotnet
Owner: raphael-group
Description: Hierarchical HotNet is an algorithm for finding hierarchies of active subnetworks.
Created: 2017-02-06 19:41:02.0
Updated: 2018-04-14 19:09:00.0
Pushed: 2018-04-14 19:04:13.0
Size: 55
Language: Python
GitHub Committers
| User | Most Recent Commit | # Commits |
|---|
Other Committers
| User | Most Recent Commit | # Commits |
|---|
Hierarchical HotNet is an algorithm for finding hierarchy of active subnetworks. While originally developed for use with cancer mutation data on protein-protein interaction networks, Hierarchical HotNet supports any application in which scores may be associated with the nodes of a network, i.e., a vertex-weighted graph.
The setup process for Hierarchical HotNet requires the following short steps:
Download Hierarchical HotNet. The following command clones the current Hierarchical HotNet repository from GitHub:
git clone https://github.com/raphael-group/hierarchical-hotnet.git
This software is either required for Hierarchical HotNet or optional but recommended for better performance or for visualizing the results.
Most likely, Hierarchical HotNet will work with other versions of the above software.
In particular, both virtualenv and GNU parallel are recommended in practice. Virtualenv provides a virtual environment that allows Python packages to be installed or updated independently of the system packages. GNU parallel facilitates running many scripts in parallel. We highly recommend running Hierarchical HotNet in parallel.
Install a Fortran compiler, such as gfortran, for better performance. The following command compiles the Fortan module used in Hierarchical HotNet:
cd src
f2py -c fortran_module.f95 -m fortran_module > /dev/null
We highly recommend using the Fortran module for better performance. However, Hierarchical HotNet will transparently fall back to a Python-only implementation if a Fortran compiler is unavailable or if compilation is unsuccessful.
Run the following code to test Hierarchical HotNet on an example network with two sets of example scores:
sh examples/example_commands.sh
This scripts requires approximately one or two minutes to finish on a modern laptop or desktop. It illustrates the full pipeline for Hierarchical HotNet on a small example. If this script completes successfully, then Hierarchical HotNet is ready to run.
Alternatively, run the following code to test Hierarchical HotNet in parallel on the same example network with the same two sets of example scores:
sh examples/example_commands_parallel.sh
It is straightforward to modify the above scripts to run Hierarchical HotNet on a compute cluster.
Hierarchical HotNet requires the use of several scripts on a few input files.
There are three input files for Hierarchical HotNet that together define a network with scores on the nodes of the network. For example, the following example defines a network with an edge between the nodes ABC and DEF, which have scores 0.5 and 0.2, respectively. For convenience, these files use the same format as the input files for HotNet2. Each file is tab separated.
This file associates each gene with an index, which we use for the edge list as well as a similarity matrix:
1 ABC
2 DEF
This file defines a network using the indices in the index-to-gene file:
1 2
This file associates each gene with a score:
ABC 0.5
DEF 0.2
We provide a script, hierarchical_hotnet.py, for running the entire Hierarchical HotNet pipeline. This script will be added shortly. This script combines the following steps, which can also be run separately:
Choose the restart parameter beta for each network by running src/choose_beta.py and create a similarity matrix for each network with the chosen beta value by running src/create_similarity_matrix.py.
Depending on your choice of null graph model, generate either permuted scores for each set of scores and network by running src/permute_scores.py or permuted networks for each network by running src/permute_networks.py. In general, it is faster to generate permuted scores than permuted networks, which also require additional similarity matrices.
Construct hierarchies on the observed network and gene scores as well as the permuted networks and gene scores by running src/construct_hierarchies.py.
Find statistically significant heights in the hierarchy by running src/plot_hierarchy_statistic.py and a statistically significant, high-effect-size cut of the hierarchy by running src/cut_hierarchy_file.py.
Perform the consensus summarization procedure on the results by running src/perform_consensus.py.
See examples/example_commands.sh or examples/example_commands_parallel.sh for a full minimal working example of Hierarchical HotNet that illustrates the use of each of these scripts, including the initial inputs and eventual outputs of the Hierarchical HotNet pipeline.
The run_hierarchical_hotnet.py script checks for intermediate results, which it will use if available. For example, if the similarity matrices are already available, then the script can skip this step.
Hierarchical HotNet finds statistically significant regions of a hierarchical clustering of genes. It also produces a statistically significant, high-effect clustering of the genes. Hierarchical HotNet provides a consensus procedure that summarizes the results from each network and set of scores.
See the examples directory for example data, scripts, and output for Hierarchical HotNet.
For support with Hierarchical HotNet, please visit the HotNet Google Group.
See LICENSE.txt for license information. Add license.
If you use Hierarchical HotNet in your work, then please cite the following reference:
Add reference.