ECSWalk: A carcinogenic driver module detection method based on a network model.
Data used in this study:
In our research, we used data processed by Rafsan Ahmed, et al. and Mark D M Leiserson, et al.
Download their research here respectively: doi.org/10.1093/bioinformatics/btz655 and doi.org/10.1038/ng.3168.
Input:
- hint_edge_file.txt: The PPI network file.
- hint_index_file.txt: The nodes to index file mapping.
- genes_vs_patients_indices_gen_paran.txt: Gene and its mutation patients.
- patients_to_indices_gen.txt: The patients name and its patient ID.
- pan12gene2freq.txt: The gene mutation frequency.
- Census_allTue_May_23_12-08-15_2017.tsv: The cosmic genes.
Run:
- compute_edge_weights.py: Compute the weights of edges between gene nodes in biological networks.
- random_walk.py: Perform a random walk on a directed weighted network.
- connected_components_isolarge.py: Driver module detection.
Outputs:
- deg_ed.txt: Mutual exclusion between connected gene nodes.
- deg_cd.txt: Coverage between connected gene nodes.
- deg_sim.txt: Similarity between connected gene nodes.
- ecswalk.txt: Weights of edges between connected gene nodes in biological networks.
- ECSWalk folder: Driver module set at different total_size thresholds.
Read more here: Source link