Code and input files for the proximity-based analysis in the "Predicting the Health Impact of Dietary Polyphenols Using a Network Medicine Framework" manuscript.
- Instructions
- Network Proximity
- Implicit Associations
- Gene Set Enrichment Analysis
- Data Files
- Citation
Clone repository
git clone https://github.com/italodovalle/polyphenols.git
Install requirements
cd polyphenols/
pip install -r requirements.txt
run_proximity.py
usage: run_proximity.py [-h] -i INTERACTOME -d DISEASEGENES -t TARGETS
[-c CORES] [-r NRANDOM] [-o OUTFOLDER]
[-sn SOURCE_NODE] [-tn TARGET_NODE] [-lcc LCC]
[-sep SEPARATOR] [-test TEST_RUN] [-chem CHEMICAL_COL]
[-prot PROTEIN_COL] [-tmp TMP_DIR]
Calculate Network Proximity
optional arguments:
-h, --help show this help message and exit
-i INTERACTOME interactome file
-d DISEASEGENES disease genes file
-t TARGETS chemical protein interactions
-c CORES number of cores to use, default = 6
-r NRANDOM n random permutations, default = 1000
-o OUTFOLDER outfolder
-sn SOURCE_NODE name source node Interactome, default = proteinA
-tn TARGET_NODE name target node Interactome, default = proteinB
-lcc LCC Consider only Interactome LCC, default = True
-sep SEPARATOR separator Interactome, default = ","
-test TEST_RUN test run
-chem CHEMICAL_COL chemical column in chemical-protein interaction file
-prot PROTEIN_COL chemical column in chemical-protein interaction file
-tmp TMP_DIR temporary folder
Example
./run_proximity.py -i data/HumanInteractome_v2017.csv -d data/PeroxisomalDisorders.csv -t data/Genistein.csv -r 1000 -sn EntrezA -tn EntrezB -chem chemical -prot entrez_id
- run time: 30 seconds
Entire dataset
./run_proximity.py -i data/HumanInteractome_v2017.csv -d data/GenesDisease.csv -t data/PolyphenolProteinInteractions.csv -c 6 -r 1000 -tmp tmp/ -sn EntrezA -tn EntrezB -test True -chem chemical -prot entrez_id
- run time: it can take several days to calculate the proximity for the entire dataset.
- Consider the example of the disease 'Cardiovascular diseases' (C14.XXX):
- The following chemicals have the 'therapeutic' label in CTD: Genistein, Kaempferol, Resveratrol, Daidzein
- Now, if you look at the specific diseases that are under 'Cardiovascular diseases' in the MeSH hierarchy we find the following chemicals with 'therapeutic' labels in CTD:
- cardiomyopathies C14.280.238 (Narigenin, Apigenin)
- cardiomyopathy, hypertrophic C14.280.484.150.070.160 (Quercetin)
- heart diseases C14.280 (-)-Epigallocatechin 3-O-gallate
- etc
- Therefore, we can consider, for example, that the association 'narigenin' and 'cardiovascular diseases' is implicit
- Therefore, we mapped all explicit associations in the MeSH hierarchy (4816 diseases) to expand explicit to implicit associations
- MeSH mapping to the different branches:
- all branches were considered
- Considering only diseases under the branch C (Diseases). Some phenotypes in CTD might be under the branch F (Psycology and Psychiatry)
- MeSH Supplementary Concept Data ID were manually mapped
- Input
data/mtrees2018.bindata/ctd_disease_chemical_phenolexplorer_therapeutic.csv
- Output:
data/ctd_polyphenols_implicit_explicit.csv
expand_associations.py
Usage
usage: expand_associations.py [-h] -m MESH -i INFILE [-o OUTFILE]
Expand associations in MeSH tree
optional arguments:
-h, --help show this help message and exit
-m MESH MeSH File
-i INFILE association table
-o OUTFILE outfile
Example:
python expand_associations.py -m data/mtrees2018.bin -i data/ctd_disease_chemical_phenolexplorer_therapeutic.csv -o data/ctd_polyphenols_implicit_explicit.csv
run_gsea.py
Usage
usage: run_gsea.py [-h] -indir INDIR -i INTERACTOME -d DISEASEGENES -t TARGETS
[-c CORES] [-r NRANDOM] [-o OUTFOLDER] [-sn SOURCE_NODE]
[-tn TARGET_NODE] [-lcc LCC] [-sep SEPARATOR]
[-chem CHEMICAL_COL] [-prot PROTEIN_COL] [-tmp TMP_DIR]
[-cell CELL_LINE]
Calculate Enrichment Score
optional arguments:
-h, --help show this help message and exit
-indir INDIR folder with .gct files
-i INTERACTOME interactome file
-d DISEASEGENES disease genes file
-t TARGETS chemical protein interactions
-c CORES number of cores to use, default = 6
-r NRANDOM n random permutations, default = 1000
-o OUTFOLDER outfolder
-sn SOURCE_NODE name source node Interactome, default = proteinA
-tn TARGET_NODE name target node Interactome, default = proteinB
-
60E9
lcc LCC Consider only Interactome LCC, default = True
-sep SEPARATOR separator Interactome, default = ","
-chem CHEMICAL_COL chemical column in chemical-protein interaction file
-prot PROTEIN_COL chemical column in chemical-protein interaction file
-tmp TMP_DIR temporary folder
-cell CELL_LINE restrict to cell line, default = MCF7
Example
./run_gsea.py -indir data/connectivity_map/ -i data/HumanInteractome_v2017.csv -d data/PeroxisomalDisorders.csv -t data/Genistein.csv -r 10 -sn EntrezA -tn EntrezB -chem chemical -prot entrez_id
- Results written in
out_gsea - Run time: It take hours/days for the entire dataset
The data folder contains:
data/HumanInteractome_v2017.csv: Human interactome (see manuscript for details)data/GenesDisease.csv: Disease-gene associations for MeSH disease terms curated in Menche et al. (2015, Science)data/PolyphenolProteinInteractions.csv: Polyphenol protein targets obtained from the STITCH databasedata/connectivity_map: Perturbation profiles obtained in the Connectivity Map database (https://clue.io/) for selected polyphenolsdata/mtrees2018.bin: MeSH hierarchyctd_disease_chemical_phenolexplorer_therapeutic.csv: all disease-polyphenol associations obtained by combining data from the databases CTD and PhenolExplorerctd_polyphenols_implicit_explicit.csv: output file of the codeexpand_associations.pyGenistein.csv: Genistein targets (for tests)PeroxisomalDisorders.csv: Peroxisomal Disorders disease genes (for tests)
do Valle, I. F. et al. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. Nat. Food 2, 143–155 (2021).