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Hierarchical HotNet

Hierarchical HotNet is an algorithm for finding hierarchies of altered 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.

Setup

The setup process for Hierarchical HotNet requires the following steps:

Download

Download Hierarchical HotNet. The following command clones the current Hierarchical HotNet repository from GitHub:

git clone https://github.com/raphael-group/hierarchical-hotnet.git

Installation

The following software is either required for Hierarchical HotNet or optional but recommended for better performance or for visualizations.

Required
Optional, but recommended

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.

Compilation

Install a Fortran compiler, such as gfortran, for better performance. The following command compiles the optional Fortan code used in Hierarchical HotNet:

cd src
f2py -c fortran_module.f95 -m fortran_module > /dev/null

We highly recommend using the Fortran code 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.

Testing

To test Hierarchical HotNet on an example network with two sets of example scores, please run the following script:

sh examples/example_commands.sh

This script illustrates the full Hierarchical HotNet pipeline. It should require less than a minute or two of CPU time, 100 MB of RAM, and 1 MB of storage space. If this script runs successfully, then Hierarchical HotNet is ready to use.

Alternatively, to run Hierarchical HotNet in parallel on the sample example data, please run the following script:

sh examples/example_commands_parallel.sh

We hightly recommend running Hierarchical HotNet in parallel. It should straightforward to modify the above scripts to run Hierarchical HotNet on a compute cluster.

Use

Hierarchical HotNet requires the use of several scripts on a few input files.

Input

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.

Index-to-gene file

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
Edge list file

This file defines a network using the indices in the index-to-gene file:

1    2
Gene-to-score file

This file associates each gene with a score:

ABC 0.5
DEF 0.2

Running

Hierarchical HotNet has several steps:

  1. Create a similarity matrix by running the src/create_similarity_matrix.py script.

  2. Create permuted data by running the src/find_permutation_bins.py and src/permute_scores.py scripts (permuted scores) or the src/permute_networks.py script (permuted networks). In general, it is faster to permute scores than networks.

  3. Construct hierarchies on observed and permuted data by running the src/construct_hierarchies.py script.

  4. Process the hierarchies by running the src/process_hierarchies.py script.

  5. Perform the consensus summarization procedure on the results by running the src/perform_consensus.py script.

See examples/example_commands.sh or examples/example_commands_parallel.sh for full minimal working examples of Hierarchical HotNet that illustrate the use of each of these scripts, including the inputs and outputs for the Hierarchical HotNet pipeline.

Output

Hierarchical HotNet identifies statistically significant regions of a hierarchical clustering of topologically close, high-scoring genes. Hierarchical HotNet also performs a consensus across hierarchical clusterings from different networks and gene scores.

Additional information

Examples

See the examples directory for example data, scripts, and output for Hierarchical HotNet.

Support

For support with Hierarchical HotNet, please visit the HotNet Google Group. Please try one of the examples in the examples directory before running Hierarchical HotNet with your own data, and please provide any error messages encountered with these examples to expedite troubleshooting.

License

See LICENSE.txt for license information.

Citation

If you use Hierarchical HotNet in your work, then please cite the following manuscript:

M.A. Reyna, M.D.M. Leiserson, B.J. Raphael. Hierarchical HotNet: identifying hierarchies of altered subnetworks. ECCB/Bioinformatics 34(17):i972-980, 2018.