FDRnet is a method for identifying significantly mutated (or differentially expressed) subnetworks in human diseases.
The setup process for FDRnet includes the following steps:
Download FDRnet. The following command clones the current FDRnet repository from GitHub:
git clone https://github.com/yangle293/FDRnet.git
The following software is required for installing FDRnet:
- Linux/Unix
- Python (2.7 or 3.5)
- NumPy (1.14)
- NetworkX (2.0)
- Matplotlib (3.1)
- CPLEX (12.7)
Academic users can obtain a free, complete version of CPLEX via IBM Academic Initiative.
After the installation of CPLEX, you also need to install the CPLEX-Python modules. Run
cd yourCPLEXhome/python/VERSION/PLATFORM
python setup.py install
There are three input files, which together define a network used in FDRnet. For example, the following files define a network containing two connected nodes A and B, which have a score of 0.5 and 0.2, respectively.
This file associates each node with an index:
1 A
2 B
This file defines a network by using the indices in the index-to-gene file:
1 2
This file associates each gene with a local FDR score:
A 0.5
B 0.2
If you have a list of p-values for individual genes, you can calculate local FDR scores using either the original R implementation (http://cran.r-project.org/web/packages/locfdr/), or the python-implementation of the locfdr method (https://github.com/leekgroup/locfdr-python). For ease of use, we provide a copy of the python implementation (locfdr-python) and a wrapper function locfdr_compute.py in example directory.
-
Compute local FDRs by running
example/locfdr_compute.pyscript. -
Run FDRnet by running
src/FDRnet_main.pyscript.
See the Examples section for a full minimal working example of FDRnet.
The output file is a .csv file organized as follows:
Seed Gene, Running Time, Optimization Status, Subnetwork
usage: FDRnet_main.py [-h] -igi INPUT_GENE_INDEX -iel INPUT_EDGE_LIST -igl
INPUT_GENE_LFDR -ofn OUTPUT_FILE_NAME -se SEED
[-bd BOUND] [-al ALPHA] [-sz SIZE]
[-tl TIME_LIMIT] [-rg RELATIVE_GAP]
-h Show help message and exit
-igi File name of the index-to-gene file
-iel File name of the input edge list file
-igl File name of the gene-to-score file
-ofn File name of output, a .csv file
-se Seed gene names, either specify a seed gene name (e.g., TP53) OR set 'all' to use all the genes with local FDRs less than FDR bound
as seeds
-bd FDR bound, default 0.1
-al Random-walk parameter, default 0.85
-sz Local graph size, default 400
-tl Time limit for each seed for solving MILP problem, default 100
-rg Relative gap in a MILP problem, default 0.01
We provide three files in the example directory: an index-to-gene file irefindex9_index_gene, an edge list file irefindex9_edge_list for the iRefIndex9.0 PPI network, and a gene-to-score file (p-value) TCGA_BRCA_SNV.txt generated by MutSig2CV applied to the TCGA breast cancer mutation data.
We can calculate the local FDRs from p-values by running the following code:
python example/locfdr_compute.py example/TCGA_BRCA_SNV.txt
There are two ways to use our algorithm. First, users can try to identify a subnetwork around any gene regardless of its local FDR score by using -se gene_name option (possibly no solution) to show a local picture of perturbation. Second, users can obtain a landscape of all perturbed regions in a PPI network by using -se all. Running with this option will return a set of significantly perturbed subnetworks around all the seeds (i.e., genes with local FDR score less than the given bound B). See the supplemental information of our paper for details. Here, we use the famous cancer gene TP53 as an example, that is, to identify a subnetwork around TP53 gene, by running the following code:
python src/FDRnet_main.py -igi example/irefindex9_index_gene -iel example/irefindex9_edge_list -igl example/TCGA_BRCA_SNV_lfdr.txt -ofn example/test.csv -se TP53
The output file look like this one:
| Seed Gene | Running Time | Optimization Status | Subnetwork |
|---|---|---|---|
| TP53 | 3.54608988762 | MIP_optimal | SP3 PTEN IRF9 GPS2 TP53 NCOR1 |
We also provide a simple script plot_subnetworks.py to visualize the identified subnetwork. The inputs are the three files in the example directory and the output file from above. For example, we can visualize the identified subnetwork around TP53 by running the code:
python src/plot_subnetworks.py -igi example/irefindex9_index_gene -iel example/irefindex9_edge_list -igl example/TCGA_BRCA_SNV_lfdr.txt -ofn example/test.csv
The result should be:
The simulation directory contains the files and codes used to generate synthetic data and plot figures in the paper. The preprocessing directory contains the preprocessing scripts used in the breast cancer study and lymphoma study. The code used to plot the results for breast cancer and lymphoma studies is same as that used in the simulation.
Please send us an email if you have any question when using FDRnet.
See LICENSE.txt for license information.
If you use FDRnet in your work, please cite the following manuscript: L. Yang, R. Chen, S. Goodison, Y. Sun. An efficient and effective method to identify significantly perturbed subnetworks in cancer, Nature Computational Science, 1, pages 79–88 (2021).