def findPathways(cvDict,gmtName, geneDict): aliasDict, dict1, dict2={}, {}, {} # set up dicts for reading KEGG files # read in kegg gene symbol dictionaries nc.parseKEGGdicthsa('inputData/hsa00001.keg',aliasDict,dict1) nc.parseKEGGdict('inputData/ko00001.keg',aliasDict,dict2) namelist=find_overlaps(gmtName,cvDict) # find list of pathways with overlaps with the genes from omics data print('num of overlap nodes: ' + str(len(namelist))) for name in namelist: retrieveGraph(name,aliasDict,dict1,dict2, cvDict, geneDict) # find and store gpickles for graphs found
def pipeline_fig_s3(filename): #Utility for supplementaryFigure3 #python2 version, uses a GMT file as input aliasDict, dict1, dict2 = {}, {}, {} # set up dicts for reading KEGG files nc.parseKEGGdicthsa('inputData/hsa00001.keg', aliasDict, dict1) #read in kegg gene symbol dictionaries nc.parseKEGGdict('inputData/ko00001.keg', aliasDict, dict2) keggDict = read_gmt(filename) # read in GMT file namelist = keggDict.keys() # retain all pathways pathwayGraphs = {} #dictionary of digraphs for name in namelist: pathwayGraphs[name] = retrieveGraph( name, aliasDict, dict1, dict2) # find and store gpickles for graphs found pickle.dump(pathwayGraphs, open('pathwayGraphs.pickle', "wb")) # save data in correct format pathwayGraphs = pickle.Unpickler(open('pathwayGraphs.pickle', "rb")).load() supplement_S3(pathwayGraphs)
Created on Thu May 14 10:28:45 2020 @author: Swapnil Keshari Summary: This file rewires the network form the Dict 2 which is imported from dataprocessing_1 file Networkx Version 2.2 """ import networkx as nx import matplotlib.pyplot as plt import networkConstructor as nc from dataprocessing_1 import Dict2 G = nx.DiGraph() #Creates a network code = '04060' aliasDict, dict1, dict2 = {}, {}, {} # set up dicts for reading KEGG files nc.parseKEGGdicthsa('inputData/hsa00001.keg', aliasDict, dict1) nc.parseKEGGdict('inputData/ko00001.keg', aliasDict, dict2) coder = str('ko' + code) nc.uploadKEGGcodes([coder], G, dict2) coder = str('hsa' + code) nc.uploadKEGGcodes_hsa([coder], G, dict1, dict2) #Check for common Genes in the graph and the data (CSV) genes Gene = set(Dict2.keys()) Gene1 = set(G.nodes()) Gene2 = Gene1.intersection(Gene) nx.write_gml(G, "04060_initial.gml") #Remove Self Edges for edge in list(G.edges()): if edge[0] == edge[1]: