Linux (tested on Ubuntu 18.04)
- SWI-Prolog version >=7, on your path as 'swipl'
- Yap version >=6.2.2, on your path as 'yap' (tested on Yap 6.2.2)
- GNU Parallel, on your path as 'parallel'
- cgcp from https://github.com/dh-tran/cgcp in order to cluster a probabilistic network into modules
- bash for executing shell scripts
Anaconda enviroment can be deployed with the environment.yml file.
usage: main.py [-h] [-path_definition_file [PATH_DEFINITION_FILE]]
[-pattern_quality_threshold [PATTERN_QUALITY_THRESHOLD]]
[-threshold_probability [THRESHOLD_PROBABILITY]]
[-alpha [ALPHA]] [-cores [CORES]] [-step [STEP]]
[-outlast [OUTLAST]] [-filter FILTER [FILTER ...]]
[cadd_file] [coverage_file] [network_file]
OMEN
positional arguments:
cadd_file CADD probabilities .pl filepath
coverage_file Coverage .pl filepath
network_file Network filepath
options:
-h, --help show this help message and exit
-path_definition_file [PATH_DEFINITION_FILE]
file specifying the path distribution
-pattern_quality_threshold [PATTERN_QUALITY_THRESHOLD]
float specifying the quality a pattern has to exceed
to be considered
-threshold_probability [THRESHOLD_PROBABILITY]
math expression for the SLP threshold
-alpha [ALPHA] value between 0 and 1 managing trade-off between the
mutex term and the gene_freq term
-cores [CORES] number of CPU threads to employ
-step [STEP] step to execute OMEN
-outlast [OUTLAST] output folder where continue OMEN run
-filter FILTER [FILTER ...]
filter specific genes
nohup python main.py new_data/CADD.pl new_data/coverage.pl new_data/network.pl > outputfile 2>&1& nohup is employed to execute the command in the background and prevent to have the shell session opened.
For more details in OMEN Usage and its construction consult the original repository OMEN: Network-based Driver Gene Identification using Mutual Exclusivity and the original paper https://doi.org/10.1093/bioinformatics/btac312.
Data files and scripts for parsing input data, visualizing and analyzing.
Auxiliar scripts to read and parse new data.
network.ipynb Create network.pl from Network data.
gene_patient_CADD.ipynb Creation of CADD files in all, aggr and non-aggr.
gene_patient_CADD_correct.ipynb Creation of CADD files in all and coverage file.
cancer_type_separator.py Separation of cancer type (aggr and non-aggr).
Files for visualization of the probabilistic networks.
probabilistic_network.ipynb Creation of subnetwork js file.
probabilistic_network_aggr_vs_non_aggr.ipynb Creation of subnetwork js file, for aggr. vs non-aggr.
To visualize network: subnetwork.html, gonetic.js, gonetic_aggr_vs_non_aggr.js and subnetwork_*.js.
Auxiliar scripts to make plots and analyze the OMEN's output.
alpha_links.ipynb Quantification of different link types for different alpha values.
alpha_vs_ranking_most_mutated_genes.ipynb Alpha vs Ranking of the most mutated genes.
clustering_plot.ipynb Gene heatmap in different clusters.
compare_rankings.ipynb Comparison of ranking with Venn diagram, Spearman, Overlap genes and Non-overlap genes, for different alpha values.
compare_rankings_same_alpha.ipynb Comparison of ranking with Venn diagram, with the same alpha values.
differences_in_gene_ranking.ipynb Differences between rankings of different alpha values.
differences_in_gene_ranking_same_alpha.ipynb Differences between rankings of same alpha values.
differences_in_gene_table.ipynb Differences between rankings of different alpha values, in a table.
gene_checker.ipynb Check if some certain genes appear in the rankings.
network_selection.ipynb Analyse different input networks with genes overlap.
probabilistic_network_aggr_vs_non_aggr.ipynb Comparison between aggr. and non-aggr networks.
probabilistic_network_aggr_vs_non_aggr_overlapping_genes.ipynb Comparison between aggr. and non-aggr networks, using gene overlap.
threshold_distribution.ipynb Evaluation of threshold distribution in a OMEN's run.
MutationFreq Mutation frequency data, plots and scripts.