HotNet
HotNet is an algorithm for finding significanlty altered subnetworks in a large gene interaction network.
People (strict random order): Fabio Vandin, Hsin-Ta Wu, Edward Rice, Layla Oesper, Adrien Deschamps, Max Leiserson, Jason Schum, Eli Upfal, Ben Raphael.
references
The HotNet algorithm is described in the following publications:
Moreover, we have used HotNet in the following publications:
download
A new version of HotNet is now available (RELEASE_NOTES):
Auxiliary files: here you can find some general files you can use to run, in particular the influence matrix derived from HPRD and general files with information for the permutation test on single nucleotide variants and copy number aberrations. These latter files contain general information (genes analyzed, approximate gene length) that should be made specific for each analyzed dataset.
contact: hotnet@cs.brown.edu
Previous versions of HotNet:
requirements
how-to
Detailed instructions for installing, configuring and running HotNet and Generalized are provided in the README.txt file in the corresponding release. Here we give the instruction to build the influence matrix for a network.
Building the influence matrix
HotNet uses an influence measure between two genes to find significant subnetworks. Since the influence depends only on the network, there is no reason to compute the influence everytime HotNet is run. Therefore you should compute the influence matrix for your network, and provide that to HotNet. Here is an how you can do it: let
A
be the adiacency matrix of the
undirected network (i.e, the entry of row i and column j is =1 if there is
an interaction between i and j). The graph Laplacian L is then given by L
= D-A, where D is a diagonal matrix with D(i,i)=degree of i in the network
(and D(i,j)=0 if i different from j). To compute the influence at time t,
you need to find the exponential of the matrix L*t (that corresponds to the heat kernel at time t). For HotNet to work, you need to assign the matrix the name 'Li', and then save it on file in Matlab format.
In Matlab, given the laplacian matrix L and the time t, you can use:
Li=expm(-L*t);
save name_file.mat Li
You will also need to generate a gene name to index in this matrix mapping
file, associating the row/column index to the gene name. See the README.txt in the release for details.
In the original publication [Vandin et al., JCB 2011] a different diffusion process was used to derive the influence, that in our tests gives results similar to the diffusion process currently used (described above). This diffusion kernel is described in:
That kernel requires the inversion of a shifted version of the laplacian matrix (refer to the original publication or to [Vandin et al., JCB 2011] for details).
visualization
Cytoscape plugin: visualize the result of HotNet.
After installing the plugin (simply copy hotnet.jar in the plugins folder of Cytoscape), you can visualize the subnetworks given by HotNet results on you current network. Choose Plugins->HotNet->Load mutated subnetworks in Cytoscape. Then choose the result file (ending with '_subnetworks.txt'), as shown below.
A dialog will open , containing a list of how many subnetworks of each size were found as well as the corresponding p-values and other information (see the README in the visualization release for details). Select a minimum subnetwork size to display, by clicking the appropriate row.
A new window will open with subnetworks dispayed in different color and grouped accordingly.
