Source code to reproduce the paper "Discovering the genes mediating the interactions between chronic respiratory diseases in the human interactome", Maiorino et al.
Note: documentation is still being prepared.
- conda
- pandas
- xlrd
- mygene
- networkx
- scipy
- tqdm
- fisher
- matplotlib
- h5py
- p_tqdm
- GEOparse
- goatools==0.8.12
- sklearn
- pyYaml
Note: While the source code of flow centrality implemented in python 3 is available in this repository, visit the companion repository https://github.com/reemagit/Flower for a dedicated implementation of Flow Centrality in python 2.6, with demos and documentation
- from command line, enter the folder where to download the source code and clone the repo
cd path/to/folder
git clone https://github.com/reemagit/flowcentrality.git
The source code will be in the directory path/to/folder/centrality
- Install a conda environment named "flowcentrality_paper" (or whatever name you prefer) with the necessary packages. The activate the environment and install the packages
conda create -n flowcentrality_paper python=3.6.7
conda activate flowcentrality_paper
pip install -r logs/requirements.txt
- Download the data with make
cd src
make download_data
- (Optional) To download the precalculated results of the paper, execute the command below
make download_results
Note: this operation is recommended since several operation are quite expensive to perform, and most results depend from intermediate files that would have to be calculated.
The package is now installed. You can use the make commands to perform the calculations and plot the figures.
Assuming that the current directory of the command line is flowcentrality/src, you can perform calculations by executing the make command make {command}, where {command} is chosen from the list below:
process_biobank: Process Biobank data
process_expression: Process all the GEO expression datasets
process_disgenet: Process DisGeNet database data
eval_fcs: Evaluate flow centrality scores
eval_paths: Evaluate paths between asthma and copd modules
eval_all_coexpression: Evaluate the sequential coexpressions of the asthma-COPD paths for each GEO dataset
eval_all_coexpression_spearman: Evaluate the spearman sequential coexpressions of the asthma-COPD paths for each GEO dataset
eval_go: Evaluate sequential similarities of the asthma-COPD paths
eval_do_sims: Evaluate Disease Ontology similarities
plot_go_asthma_copd: Sequential similarity of asthma-COPD paths
plot_go_asthma_copd_bp_mf_cc: Sequential similarity of asthma-COPD paths partitioned in BP/MF/CC
plot_disease_pairs_go: Sequential similarity of disease pairs paths
plot_disease_pairs_go_pvals_vert: P-values of sequential similarity of disease pairs paths
plot_disease_sims_heatmap: Similarities between diseases
plot_disease_sims_distribution: Distribution of similarities between diseases
plot_diseases_vs_rdmgenesets_pvals_heatmap: Sequential similarity of disease pairs compared to random sets
plot_coexpr_disease_boxplots: Sequential coexpression of asthma-COPD paths (disease conditions)
plot_coexpr_healthy_boxplots: Sequential coexpression of asthma-COPD paths (healthy conditions)
plot_ukb_overlap_pvals: Cutoff of asthma and COPD DIAMOnD ranking
plot_asthma_pneumonia_ipf: Sequential coexpression of asthma-pneumonia, asthma-IPF and asthma-COPD paths
plot_copd_pneumonia_ipf: Sequential coexpression of copd-pneumonia, copd-IPF and asthma-COPD paths
plot_asthma_copd_random_diseases_gse4302_boxplot: Sequential coexpression of asthma-COPD paths compared to asthma-random diseases
plot_asthma_copd_random_diseases_gse57148_boxplot: Sequential coexpression of asthma-COPD paths compared to COPD-random diseases
plot_coexpr_scores_asthmacopd: Significance scores of sequential coexpression for each GEO dataset
plot_GSE4302_boxplot: Sequential coexpression of asthma-COPD paths in GSE4302 dataset
plot_GSE57148_boxplot: Sequential coexpression of asthma-COPD paths in GSE57148 dataset
plot_disease_pairs_go_pvals_horiz;: Significance scores of sequential coexpression between related disease pairs