Network motif discovery is a well-established statistical method that identifies over represented sub-networks or "motifs'' within a larger network. This concept is a widely-used hypothesis generation tool to identify cases in which a small genetic sub-network is unusually frequently observed in a sample because it is serving a particular biological function.
Network motif discovery algorithms operate by comparing the frequency of particular sub-network of interest within a given "real-world'' network to its frequency in a large collection of randomized networks. While the methods of randomization may differ, all algorithms face the challenge of how to sample uniformly and independently from the set of all possible randomized networks that may be generated. To date, there has been no sound practical method to numerically evaluate whether any current sampling strategy performs as intended by achieving both uniform and independent sampling. As a result, it was not previously possible to determine whether a given network motif finding algorithm was appropriate for a given network of interest— thus it was not possible to assess the validity of resulting hypotheses. In this work we present IndeCut, the first and only method to date that allows characterization of network motif finding algorithm performance. IndeCut further allows a user to determine the number of samples that are appropriate for a network of interest for any network motif finding algorithm. IndeCut is open source software.
- IndeCut software can be run on Linux operating systems.
- Make sure that Java, python, and R is installed on your computer.
1. Download indecut.zip
2. Unzip indecut.zip into your working directory.
3. Change path to the indecut folder as follows:
4. Open the file
start_running_indecut.shand make changes as follows:
- set PYTHON parameter to the path that Python is installed on your computer.
5. Run the software as follows:
6. When the running finished find the cut norm estimates in the following path: