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Find_Common_Paths_diffKGs.py
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Find_Common_Paths_diffKGs.py
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#Outputs the patterns of nodes that describe the shortest path between a specific set of pairs (microbe - metabolie). Skim only lists each node type once, full lists every occurance of a node type in the shortest path found.
import csv
import pandas as pd
import argparse
import os
import glob
from collections import Counter
from create_graph import create_graph
from collections import defaultdict
#Define arguments for each required and optional input
def defineArguments():
parser=argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--directory",dest="Directory",required=True,help="Directory")
parser.add_argument("--graph-type",dest="GraphType",required=True,help="GraphType")
parser.add_argument("--full-or-skim",dest='FullOrSkim',required=False,help="FullOrSkim",default='skim')
parser.add_argument("--output-patterns",dest='OutputPatterns',required=False,help="OutputPatterns",default=True)
return parser
###Read in all files
def process_files(csv_file,labels_df,full_or_skim,ont_types,output_patterns):
pathway_df = pd.read_csv(csv_file,sep='|')
print(csv_file)
#Only return pattern if it exists
if len(pathway_df) > 0:
if full_or_skim == 'skim':
#### To look at content of all paths easily ####
if not output_patterns:
pattern = pathway_df.iloc[0].loc['S'] #'P'] for checking edge types
for i in range(0,len(pathway_df)):
if pathway_df.iloc[i].loc['S'] not in pattern: #'P'] not in pattern:
pattern = pattern + " --- " + pathway_df.iloc[i].loc['S'] #'P']
if pathway_df.iloc[i].loc['O'] not in pattern: #'P'] not in pattern:
pattern = pattern + " --- " + pathway_df.iloc[i].loc['O'] #'P']
if output_patterns:
#### To look at patterns of paths ###
pattern = check_ont_type(pathway_df.iloc[0].loc['S'],ont_types,labels_df)
for i in range(0,len(pathway_df)):
if check_ont_type(pathway_df.iloc[i].loc['S'],ont_types,labels_df) not in pattern:
pattern = pattern + " --- " + check_ont_type(pathway_df.iloc[i].loc['S'],ont_types,labels_df)
if check_ont_type(pathway_df.iloc[i].loc['O'],ont_types,labels_df) not in pattern:
pattern = pattern + " --- " + check_ont_type(pathway_df.iloc[i].loc['O'],ont_types,labels_df)
#alphabetize order of patterns so that there are no duplicates, only interested in the content not the order
i_list = sorted(pattern.split(' --- '))
pattern = i_list[0]
for i in range(1,len(i_list)):
pattern = pattern + " --- " + i_list[i]
#Add final node in pattern
#print('final node: ',pathway_df.iloc[len(pathway_df)-1].loc['O'])
#if check_ont_type(pathway_df.iloc[len(pathway_df)-1].loc['O'],ont_types,labels_df) not in pattern:
# pattern = pattern + " --- " + check_ont_type(pathway_df.iloc[len(pathway_df)-1].loc['O'],ont_types,labels_df)
elif full_or_skim == 'full':
pattern = check_ont_type(pathway_df.iloc[0].loc['S'],ont_types,labels_df) + " --- " + pathway_df.iloc[0].loc['P'] + " --- " + check_ont_type(pathway_df.iloc[0].loc['O'],ont_types,labels_df)
for i in range(1,len(pathway_df)):
#print(pathway_df.iloc[i].loc['O'],pathway_df.iloc[i].loc['O'])
#print(check_ont_type(pathway_df.iloc[i].loc['O'],ont_types,labels_df))
new_triple = " --- " + pathway_df.iloc[i].loc['P'] + " --- " + check_ont_type(pathway_df.iloc[i].loc['O'],ont_types,labels_df)
pattern = pattern + new_triple
else:
pattern = 'none'
name = csv_file.split('.csv')[0]
return pattern,name
def check_ont_type(node,ont_types,labels_df):
label = labels_df.loc[labels_df['label'] == node,'id'].values[0]
for i in list(ont_types.keys()):
if i in label:
return ont_types[i]
def get_path_length(csv_file):
pathway_df = pd.read_csv(csv_file,sep='|')
path_length = len(pathway_df)
return path_length
def main():
#Generate argument parser and define arguments
parser = defineArguments()
args = parser.parse_args()
directory = args.Directory
graph_type = args.GraphType
full_or_skim = args.FullOrSkim
output_patterns = args.OutputPatterns
microbe_phenio_triples_file = '/Users/brooksantangelo/Documents/HunterLab/Exploration/kg_microbe_phenio/output_data/merged-kg/kgx_merged-kg_edges.tsv'
microbe_phenio_labels_file = '/Users/brooksantangelo/Documents/HunterLab/Exploration/kg_microbe_phenio/output_data/merged-kg/kgx_merged-kg_nodes.tsv'
mgmlink_triples_file = '/Users/brooksantangelo/Documents/HunterLab/MGMLink/git/MGMLink/Output/PheKnowLator_v3.0.2_full_instance_relationsOnly_OWLNETS_Triples_Identifiers_withGutMGene_withMicrobes.txt'
mgmlink_labels_file = '/Users/brooksantangelo/Documents/HunterLab/MGMLink/git/MGMLink/Output/PheKnowLator_v3.0.2_full_instance_relationsOnly_OWLNETS_NodeLabels_NewEntities.txt'
if graph_type == 'kg-covid19':
graph = [microbe_phenio_triples_file,microbe_phenio_labels_file]
ont_types = {'CHEBI:':'CHEBI','PR:':'PRO','MONDO:':'MONDO','/hgnc/':'hgnc','CL:':'CLO','CARO:':'CARO','BSPO:':'BSPO','NCBITaxon:':'NCBITaxon/ContextualMicrobe','BTO:':'BTO','GO:':'GO','CHR:':'CHR','FBbt:':'FBbt','FMA:':'FMA','HP:':'HPO','MA:':'MA','MP:':'MPO','OBA:':'OBA','PATO:':'PATO','PLANA:':'PLANA','UBERON:':'UBERON','UPHENO:':'UPHENO','WBbt:':'WBbt','ZP:':'ZP','ENSEMBL:':'ENSEMBL','CHEMBL.':'CHEMBL','NBO:':'MONDO','ENVO:':'ENVO','ECOCORE:':'ECOCORE','MFOMD:':'MONDO','BFO:':'BFO'}
if graph_type == 'pkl':
graph = [mgmlink_triples_file,mgmlink_labels_file]
ont_types = {'pkt/':'NCBITaxon/ContextualMicrobe','/CHEBI_':'CHEBI','/PR_':'PRO','/PW_':'REACTOME_PW','/gene':'gene','/MONDO_':'MONDO','/HP_':'HPO','/VO_':'vaccine entity','/EFO_':'EFO','FAKEURI_':'other chemical','NCBITaxon_':'NCBITaxon/ContextualMicrobe','/GO_':'GO','/DOID_':'MONDO','NBO_':'MONDO'}
g = create_graph(graph[0],graph[1])
csv_files = glob.glob(os.path.join(directory, "*Subgraph*.csv"))
patterns_all = []
names_all = []
path_lengths = defaultdict(list)
for f in csv_files:
pattern,name = process_files(f,g.labels_all,full_or_skim,ont_types,output_patterns)
path_length = get_path_length(f)
if pattern != 'none':
patterns_all.append(pattern)
path_lengths[pattern].append(path_length)
names_all.append(name)
#Get count of each pattern
patterns_count = dict(Counter(patterns_all))
#Create df of Pattern/Name/count
patterns_all_df = pd.DataFrame({'Pattern':patterns_all})
patterns_all_df['Name'] = names_all
counts = []
for i in range(len(patterns_all_df)):
counts.append(patterns_count[patterns_all_df.iloc[i].loc['Pattern']])
#d = {}
#p = patterns_all_df.iloc[i].loc['Pattern']
#d['Count'] = patterns_count[p]
#patterns_all_df = patterns_all_df.append(d,ignore_index=True)
path_lengths_str = []
patterns_all_df['Count'] = counts
for i in range(len(patterns_all_df)):
p = patterns_all_df.iloc[i].loc['Pattern']
p_str = ','.join(map(str,path_lengths[p]))
path_lengths_str.append(p_str)
patterns_all_df['Path_Length'] = path_lengths_str
patterns_all_df = patterns_all_df.sort_values(by=['Pattern','Count'])
#patterns_df = pd.DataFrame.from_dict(patterns_count, orient='index',columns = ['Count'])
#patterns_df.reset_index(inplace=True)
#patterns_df = patterns_df.rename(columns = {'index':'Pattern'})
#Generate df of only the patterns/no filename to get unique patterns
patterns_all_unique = patterns_all_df.Pattern.drop_duplicates()
if full_or_skim == 'skim':
patterns_all_df.to_csv(directory+'/Patterns_Counts_Skim.csv',sep=',',index=False) #Patterns_ #PathLabel_ if outputting the actual paths, not the patterns
patterns_all_unique.to_csv(directory+'/Patterns_Counts_Skim_Unique.csv',sep=',',index=False) #Patterns_ #PathLabel_ if outputting the actual paths, not the patterns
elif full_or_skim == 'full':
patterns_all_df.to_csv(directory+'/Pattern_Counts_Full.csv',sep=',',index=False)
patterns_all_unique.to_csv(directory+'/Pattern_Counts_Full_Unique.csv',sep=',',index=False)
if __name__ == '__main__':
main()