def printOutput(coreGraph, graphGenes):
if not os.path.exists('networkOutput'):
os.mkdir('networkOutput')
#write graph in a .txt file
time = dt.datetime.now()
printTime = time.strftime("%Y%m%d%H%M%S")
os.mkdir('networkOutput/' + printTime)
print('Creating directory: \'networkOutput/' + printTime + '\'',
flush=True)
#Pearson correlation
print('Calculating Pearson Correlation...', flush=True)
#only on complete graph because all edges in core are in the complete graph
pearsonComplete = ut.pearsonCorrelation(graphGenes, 'vv_exprdata_2.csv')
#RETURN CORE
nameFileCore = 'networkOutput/' + printTime + '/Core_Graph'
coreGraph = sorted(coreGraph, key=ut.ord)
ut.printCSV(nameFileCore, coreGraph)
#RETURN GRAPH
nameFileCompleteGraph = 'networkOutput/' + printTime + '/Complete_Graph'
graphGenes = sorted(graphGenes, key=ut.ord)
ut.printCSV(nameFileCompleteGraph, graphGenes)
#DRAW GRAPH
graphic.drawGraph(coreGraph, nameFileCore + '_Circular', pearsonComplete,
autoSaveImg, [], 1 - min_frel, False)
graphic.drawGraph(coreGraph, nameFileCore, pearsonComplete, autoSaveImg,
[], 1 - min_frel, True)
graphic.drawGraph(graphGenes, nameFileCompleteGraph + '_Circular',
pearsonComplete, autoSaveImg, list_Genes, 1 - min_frel,
False)
graphic.drawGraph(graphGenes, nameFileCompleteGraph, pearsonComplete,
autoSaveImg, list_Genes, 1 - min_frel, True)
def printOutput(coreGraph, graphGenes):
if not os.path.exists('networkOutput'):
os.mkdir('networkOutput')
#write graph in a .txt file
time = dt.datetime.now()
printTime = time.strftime("%Y%m%d%H%M%S")
os.mkdir('networkOutput/' + printTime)
print('Creating directory: \'networkOutput/' + printTime + '\'',
flush=True)
#Pearson correlation
print('Calculating Pearson Correlation...', flush=True)
#only on complete graph because all edges in core are in the complete graph
pearsonComplete = ut.pearsonCorrelation(graphGenes, 'vv_exprdata_2.csv')
#UPDATE NAME GENE
f = open('import_doc/NewAnnotVitisnet3.csv', 'r')
text = f.readlines()
listLineName = []
i = 1
while i < len(text):
listLineName.append(text[i].split(','))
i += 1
listBioNameUpdate = {}
for l in listLineName:
if l[3] != '':
if l[3] not in listBioNameUpdate.values():
listBioNameUpdate[l[0].upper()] = l[3]
else:
listBioNameUpdate[l[0].upper()] = l[3] + '_' + l[0].upper()
elif l[2] != '':
if l[2] not in listBioNameUpdate.values():
listBioNameUpdate[l[0].upper()] = l[2]
else:
listBioNameUpdate[l[0].upper()] = l[2] + '_' + l[0].upper()
else:
listBioNameUpdate[l[0].upper()] = l[0].upper()
f.close()
i = 0
while i < len(graphGenes):
# tmp = graphGenes[i]
# try:
# graphGenes[i] = (listBioNameUpdate[tmp[0]], listBioNameUpdate[tmp[1]], tmp[2])
# except:
# try:
# graphGenes[i] = (tmp[0], listBioNameUpdate[tmp[1]], tmp[2])
# except:
# graphGenes[i] = (listBioNameUpdate[tmp[0]], tmp[1], tmp[2])
tmp = pearsonComplete[i]
try:
pearsonComplete[i] = (listBioNameUpdate[tmp[0]],
listBioNameUpdate[tmp[1]], tmp[2])
except:
try:
pearsonComplete[i] = (tmp[0], listBioNameUpdate[tmp[1]],
tmp[2])
except:
pearsonComplete[i] = (listBioNameUpdate[tmp[0]], tmp[1],
tmp[2])
i += 1
#RETURN CORE
nameFileCore = 'networkOutput/' + printTime + '/Core_Graph'
coreGraph = sorted(coreGraph, key=ut.ord)
ut.printCSV(nameFileCore, coreGraph)
#update name of genes
i = 0
while i < len(coreGraph):
tmp = coreGraph[i]
try:
coreGraph[i] = (listBioNameUpdate[tmp[0]],
listBioNameUpdate[tmp[1]], tmp[2])
except:
try:
coreGraph[i] = (tmp[0], listBioNameUpdate[tmp[1]], tmp[2])
except:
coreGraph[i] = (listBioNameUpdate[tmp[0]], tmp[1], tmp[2])
i += 1
coreGraph = sorted(coreGraph, key=ut.ord)
#draw graph in a image
graphic.drawGraph('V', coreGraph, nameFileCore + '_Circular',
pearsonComplete, autoSaveImg, [], 1 - min_frel, False,
False, True)
graphic.drawGraph('V', coreGraph, nameFileCore, pearsonComplete,
autoSaveImg, [], 1 - min_frel, False, True, True)
#RETURN GRAPH
nameFileCompleteGraph = 'networkOutput/' + printTime + '/Complete_Graph'
graphGenes = sorted(graphGenes, key=ut.ord)
ut.printCSV(nameFileCompleteGraph, graphGenes)
#update name
i = 0
while i < len(graphGenes):
tmp = graphGenes[i]
try:
graphGenes[i] = (listBioNameUpdate[tmp[0]],
listBioNameUpdate[tmp[1]], tmp[2])
except:
try:
graphGenes[i] = (tmp[0], listBioNameUpdate[tmp[1]], tmp[2])
except:
graphGenes[i] = (listBioNameUpdate[tmp[0]], tmp[1], tmp[2])
i += 1
graphGenes = sorted(graphGenes, key=ut.ord)
#draw graph in a image
for k in list_Genes:
list_Genes[list_Genes.index(k)] = listBioNameUpdate[k]
graphic.drawGraph('V', graphGenes, nameFileCompleteGraph + '_Circular',
pearsonComplete, autoSaveImg, list_Genes, 1 - min_frel,
False, False, True)
graphic.drawGraph('V', graphGenes, nameFileCompleteGraph, pearsonComplete,
autoSaveImg, list_Genes, 1 - min_frel, False, True, True)
def printOutput(coreGraph, graphGenes, graphGenesOld):
if not os.path.exists('networkOutput'):
os.mkdir('networkOutput')
#write graph in a .txt file
time = dt.datetime.now()
printTime = time.strftime("%Y%m%d%H%M%S")
os.mkdir('networkOutput/' + printTime)
print('Creating directory: \'networkOutput/' + printTime + '\'',
flush=True)
#Pearson correlation
print('Calculating Pearson Correlation...', flush=True)
#only on complete graph because all edges in core are in the complete graph
if comprimeNode:
fIsoEdges = open('networkOutput/' + printTime + '/isoformInEdge.txt',
'w')
fIsoEdgesCSV = open(
'networkOutput/' + printTime + '/isoformInEdge.csv', 'w')
listEdgesOriginalGraph = [(a, b) for (a, b, c) in graphGenesOld]
prePearson = {}
for e in graphGenes:
numberIsoformPerEdges = 0
fIsoEdges.write('EDGE: ' + str(e[0]) + ' - ' + str(e[1]) +
'. Componed by:\n')
fIsoEdgesCSV.write(str(e[0][1:]) + ',' + str(e[1][1:]))
listIndex1 = indexDictGene(e[0], listBioNameUpdate.values())
listIndex2 = indexDictGene(e[1], listBioNameUpdate.values())
for k in [(a, b) for a in listIndex1 for b in listIndex2]:
if k in listEdgesOriginalGraph:
fIsoEdges.write('> ' + str(listBioNameUpdate[k[0]]) +
' - ' + str(listBioNameUpdate[k[1]]) +
'\n')
fIsoEdgesCSV.write(',' + str(listBioNameUpdate[k[0]]) +
'-' + str(listBioNameUpdate[k[1]]))
numberIsoformPerEdges += 1
prePearson[k] = 1
fIsoEdges.write('NUMBER OF EDGES: ' + str(numberIsoformPerEdges) +
'\n\n')
fIsoEdgesCSV.write('\n')
fIsoEdges.close()
fIsoEdgesCSV.close()
pearsonComplete = ut.pearsonCorrelation(
list(prePearson.keys()), 'hgnc_cc_zero_filtered_mat.csv')
i = 0
while i < len(pearsonComplete):
tmp = pearsonComplete[i]
pearsonComplete[i] = ((re.search(
r'@\w*', listBioNameUpdate[tmp[0]])).group(), (re.search(
r'@\w*', listBioNameUpdate[tmp[1]])).group(), tmp[2])
i += 1
pearsonComplete = ut.manageDuplicates(pearsonComplete)
else:
pearsonComplete = [] #TCGA
if typeDB:
pearsonComplete = ut.pearsonCorrelation(
graphGenes, 'hgnc_cc_zero_filtered_mat.csv') #FANTOM
#update name of genes
i = 0
while i < len(coreGraph):
tmp = coreGraph[i]
if tmp[0] in listBioNameUpdate.keys(
) and tmp[1] in listBioNameUpdate.keys():
coreGraph[i] = (listBioNameUpdate[tmp[0]],
listBioNameUpdate[tmp[1]], tmp[2])
i += 1
i = 0
while i < len(graphGenes):
tmp = graphGenes[i]
if tmp[0] in listBioNameUpdate.keys(
) and tmp[1] in listBioNameUpdate.keys():
graphGenes[i] = (listBioNameUpdate[tmp[0]],
listBioNameUpdate[tmp[1]], tmp[2])
if typeDB:
tmp = pearsonComplete[i] #FANTOM
if tmp[0] in listBioNameUpdate.keys(
) and tmp[1] in listBioNameUpdate.keys():
pearsonComplete[i] = (listBioNameUpdate[tmp[0]],
listBioNameUpdate[tmp[1]], tmp[2]
) #FANTOM
else:
if tmp[0] in listBioNameUpdate.keys(
) and tmp[1] in listBioNameUpdate.keys():
pearsonComplete[i] = (listBioNameUpdate[tmp[0]],
listBioNameUpdate[tmp[1]], 1) #TCGA
else: #TCGA
pearsonComplete.append((tmp[0], tmp[1], 1)) #TCGA
i += 1
print('Calculating Pearson Correlation complete', flush=True)
#RETURN CORE
nameFileCore = 'networkOutput/' + printTime + '/Core_Graph'
coreGraph = sorted(coreGraph, key=ut.ord)
ut.printCSV(nameFileCore, coreGraph)
#draw graph in a image
graphic.drawGraph('H', coreGraph, nameFileCore + '_Circular',
pearsonComplete, autoSaveImg, [], 1 - min_frel,
comprimeNode, False, typeDB)
graphic.drawGraph('H', coreGraph, nameFileCore, pearsonComplete,
autoSaveImg, [], 1 - min_frel, comprimeNode, True,
typeDB)
#RETURN GRAPH
nameFileCompleteGraph = 'networkOutput/' + printTime + '/Complete_Graph'
graphGenes = sorted(graphGenes, key=ut.ord)
ut.printCSV(nameFileCompleteGraph, graphGenes)
#draw graph in a image
graphic.drawGraph('H', graphGenes, nameFileCompleteGraph + '_Circular',
pearsonComplete, autoSaveImg, list_Genes, 1 - min_frel,
comprimeNode, False, typeDB)
graphic.drawGraph('H', graphGenes, nameFileCompleteGraph, pearsonComplete,
autoSaveImg, list_Genes, 1 - min_frel, comprimeNode,
True, typeDB)
def main():
if len(sys.argv) >= 2:
if sys.argv[1] == '--help':
utex.printInfo()
elif sys.argv[1] == '-vitis' and (sys.argv[2] == '-shared'
or sys.argv[2] == '-rank'
or sys.argv[2] == '-frel'
or sys.argv[2] == '-pattern'):
#read parameters
cmd = readParameters(sys.argv)
analyzeAllGene = False
for f in cmd[1]:
if f[0] == '-f':
global min_frel
min_frel = float(f[1])
elif f[0] == '-a':
global autoSaveImg
autoSaveImg = True
elif f[0] == '-c':
global completeGraph
completeGraph = True
elif f[0] == '-e':
global printDiagram
printDiagram = True
elif f[0] == '-u':
analyzeAllGene = True
#read each file .csv, read first line, check if is our gene
#If YES -> go further, If NO -> check next file
#When we have read all expansion of all gene, manage lists
#find common genes in the lists readed
listCommonGenes = []
edgesGraph = []
listCouple = []
if analyzeAllGene:
#read files Vitis
listFiles = utex.readFilesGenes(cmd[0][:], listCouple, cmd[1])
listCouple = [[u[0] for u in listFiles]]
else:
listCouple = utex.readFiles(cmd[0][0])
#read files Vitis
listFiles = utex.readFilesGenes(cmd[0][1:], listCouple, cmd[1])
#find common genes
listCommonGenes = utex.findCommonGenes(listCouple, listFiles)
if typeAnalyze == 0 or typeAnalyze == 1 or typeAnalyze == 3: #frel or rank or pattern
edgesGraph = utex.buildEdgesFrelRankPattern(
listCouple, listFiles)
elif typeAnalyze == 2: #shared
edgesGraph = listCommonGenes[0]
#print CSV with genes share between every gene of LGN
createDir()
utex.printCSV(edgesGraph, listCommonGenes[1], nameDir, typeAnalyze)
#Draw the Venn diagram, Histogram
if printDiagram:
utex.printNumberVenn(listCommonGenes, nameDir)
graphic.printVenn(listCommonGenes[1], listCouple, nameDir)
if analyzeAllGene:
textFiles = utex.readFilesGenes(cmd[0][:], listCouple,
[('-f', min_frel)])
else:
textFiles = utex.readFilesGenes(cmd[0][1:], listCouple,
[('-f', min_frel)])
graphic.printHistogram(edgesGraph, textFiles, nameDir)
pearsonComplete = []
for l in edgesGraph:
#Write genes to download and add the edges
if completeGraph:
#write in a .txt the genes to download
listFileToRead = utex.nameAssociateGene(l)
if len(listFileToRead) > 0:
strToPrint = "To draw edges between associated genes please download lists in file \'" + nameDir + str(
edgesGraph.index(l)) + "/listToDownload.txt"
f = open(
nameDir + str(edgesGraph.index(l)) +
'/listToDownload.txt', 'w')
for g in listFileToRead:
f.write(str(g) + ', ')
f.close()
print(strToPrint, flush=True)
#wait until they gave the path
print('Please write the path of file zip:', flush=True)
path = input('>>')
print('FILE ZIP: ' + path, flush=True)
#read file and find edges between them
matrixGenes = vit.buildMatrixGenesVitis(
[('-f', min_frel)], [path])
print('Finding edges between genes...')
graphGenes = ut.manageDuplicates(
ut.buildGraph(matrixGenes))
print('Process complete')
#save new edges in the list
#create dir for each couple of genes
nameDirGenes = nameDir + str(edgesGraph.index(l)) + '/'
#Write file .csv
f = open(nameDirGenes + 'edges_graph' + '.csv', 'a')
f.write('Edges between discovered genes\n')
f.write('GeneA,rank,frel,GeneB\n')
i = 0
while i < len(graphGenes):
try:
rank = matrixGenes[[u[0] for u in matrixGenes
].index(graphGenes[i][0])]
rank = rank[[v[1] for v in rank[1:]
].index(graphGenes[i][1])]
except:
rank = matrixGenes[[u[0] for u in matrixGenes
].index(graphGenes[i][1])]
rank = rank[[v[1] for v in rank[1:]
].index(graphGenes[i][0])]
#f.write(str(listBioNameUpdate[graphGenes[i][0]])+','+str(rank[0])+','+str(listBioNameUpdate[graphGenes[i][1]])+','+str(graphGenes[i][2])+'\n')
f.write(
str(graphGenes[i][0]) + ',' + str(rank[0]) +
',' + str(graphGenes[i][2]) + ',' +
str(graphGenes[i][1]) + '\n')
l.append((graphGenes[i][0], rank[0],
graphGenes[i][1], graphGenes[i][2]))
i += 1
f.close()
#Calculating pearson correlation for each edge
print('Calculating Pearson correlation...')
listForPearson = [(a, c, d) for (a, b, c, d) in l[1:]]
pearsonComplete.append(
ut.pearsonCorrelation(listForPearson, 'vv_exprdata_2.csv'))
print('Pearson Correlation done')
#Draw graph
graphic.printCommonGraph(edgesGraph, pearsonComplete, 1 - min_frel,
nameDir, autoSaveImg)
else:
print('ERROR: wrong format')
utex.printInfo()
def main():
if len(sys.argv) >= 2:
if sys.argv[1] == '--help':
utex.printInfo()
elif (sys.argv[1] == '-fantom' or sys.argv[1] == '-TCGA' or sys.argv[1] == '-vitis') and (sys.argv[2] == '-shared' or sys.argv[2] == '-rank' or sys.argv[2] == '-frel'):
#read parameters
cmd = readParameters(sys.argv)
for f in cmd[1]:
if f[0] == '-f':
global min_frel
min_frel = float(f[1])
elif f[0] == '-a':
global autoSaveImg
autoSaveImg = True
elif f[0] == '-c':
global completeGraph
completeGraph = True
elif f[0] == '-e':
global printDiagram
printDiagram = True
#read each file .csv, read first line, check if is our gene
#If YES -> go further, If NO -> check next file
#When we have read all expansion of all gene, manage lists
#TODO: Fix better. Take VIT as LGN in input
try:
listCouple = [[listBioNameUpdate[elem.strip().upper()] for elem in u] for u in utex.readFiles(cmd[0][0])]
except:
listCouple = utex.readFiles(cmd[0][0])
#find common genes in the lists readed
listCommonGenes = []
isoformInEdge = []
edgesGraph = []
if TCGAdb or vitis:
#read files if are TCGA or Vitis
listFiles = [utex.manageBR(u) for u in utex.readFilesGenes(cmd[0][1:], listCouple, cmd[1], vitis, TCGAdb, listBioNameUpdate)]
if vitis:
#upload name if is Vitis
for l in listFiles:
l[0] = listBioNameUpdate[l[0]]
i = 1
while i < len(l):
try:
l[i] = (l[i][0], listBioNameUpdate[l[i][1]], l[i][2])
except:
pass
i += 1
#find common genes
listCommonGenes = utex.findCommonGenes(listCouple, listFiles)
if typeAnalyze == 0 or typeAnalyze == 1: #frel or rank
edgesGraph = utex.buildEdgesFrelRank(listCouple, listFiles)
elif typeAnalyze == 2: #shared
edgesGraph = listCommonGenes[0]
else:
#read files if is Fantom
listFiles = utex.readFilesGenes(cmd[0][1:-1], listCouple, cmd[1], vitis, TCGAdb, listBioNameUpdate)
#upload name
for l in listFiles:
l[0] = listBioNameUpdate[l[0]]
i = 1
while i < len(l):
l[i] = (l[i][0], listBioNameUpdate[l[i][1]], l[i][2])
i += 1
#read which isoforms compone the edges
isoformInEdge = utex.readFiles(cmd[0][-1])
#find common genes
listCommonGenes = utex.findCommonGenesFantom(listCouple, listFiles, isoformInEdge)
if typeAnalyze == 0 or typeAnalyze == 1: #frel or rank
edgesGraph = utex.buildEdgesFrelRankIsoform(listCouple, listFiles, isoformInEdge)
elif typeAnalyze == 2:
edgesGraph = listCommonGenes[0]
#print CSV with genes share between every gene of LGN
createDir()
utex.printCSV(edgesGraph, listCommonGenes[1], nameDir, listBioNameUpdate)
#Draw the Venn diagram, Histogram
if printDiagram:
utex.printNumberVenn(listCommonGenes, nameDir)
graphic.printVenn(listCommonGenes[1], listCouple, nameDir)
if TCGAdb or vitis:
textFiles = [utex.manageBR(u) for u in utex.readFilesGenes(cmd[0][1:], listCouple, [('-f',0.1)], vitis, TCGAdb, listBioNameUpdate)]
if vitis:
#upload name if is Vitis
for l in textFiles:
l[0] = listBioNameUpdate[l[0]]
i = 1
while i < len(l):
try:
l[i] = (l[i][0], listBioNameUpdate[l[i][1]], l[i][2])
except:
pass
i += 1
else:
textFiles = utex.readFilesGenes(cmd[0][1:-1], listCouple, [('-f',0.1)], vitis, TCGAdb, listBioNameUpdate)
#upload name
for l in textFiles:
l[0] = listBioNameUpdate[l[0]]
i = 1
while i < len(l):
l[i] = (l[i][0], listBioNameUpdate[l[i][1]], l[i][2])
i += 1
graphic.printHistogram(edgesGraph, textFiles, nameDir, TCGAdb or vitis, isoformInEdge)
pearsonComplete = []
for l in edgesGraph:
#Write genes to download and add the edges
if completeGraph:
#write in a .txt the genes to download
listFileToRead = utex.nameAssociateGene(l, listBioNameUpdate, TCGAdb)
if len(listFileToRead) > 0:
strToPrint = "To draw edges between associated genes please download lists in file \'"+nameDir+str(edgesGraph.index(l))+"/listToDownload.txt"
f = open(nameDir+str(edgesGraph.index(l))+'/listToDownload.txt', 'w')
for g in listFileToRead:
f.write(str(g) + ', ')
f.close()
print(strToPrint, flush=True)
#wait until they gave the path
print('Please write the path of file zip:', flush=True)
path = input('>>')
print('FILE ZIP: '+path, flush=True)
if vitis:
#read file and find edges between them
matrixGenes = vit.buildMatrixGenesVitis([('-f', min_frel)], [path])
print('Finding edges between genes...')
graphGenes = ut.manageDuplicates(ut.buildGraph(matrixGenes))
print('Process complete')
#save new edges in the list
nameF = utex.buildNamefile(l)
#create dir for each couple of genes
nameDirGenes = nameDir+str(edgesGraph.index(l))+'/'
#Write file .csv
nameF = nameF.replace("<", "_")
nameF = nameF.replace(">", "_")
f = open(nameDirGenes+'edges_graph'+'.csv', 'a')
f.write('Edges between discovered genes\n')
f.write('GeneA,rank,frel,GeneB\n')
i = 0
while i < len(graphGenes):
try:
rank = matrixGenes[[u[0] for u in matrixGenes].index(graphGenes[i][0])]
rank = rank[[v[1] for v in rank[1:]].index(graphGenes[i][1])]
except:
rank = matrixGenes[[u[0] for u in matrixGenes].index(graphGenes[i][1])]
rank = rank[[v[1] for v in rank[1:]].index(graphGenes[i][0])]
#f.write(str(listBioNameUpdate[graphGenes[i][0]])+','+str(rank[0])+','+str(listBioNameUpdate[graphGenes[i][1]])+','+str(graphGenes[i][2])+'\n')
f.write(str(graphGenes[i][0])+','+str(rank[0])+','+str(graphGenes[i][2])+','+str(graphGenes[i][1])+'\n')
l.append((listBioNameUpdate[graphGenes[i][0]], rank[0], listBioNameUpdate[graphGenes[i][1]], graphGenes[i][2]))
i += 1
f.close()
else:
pass
#TODO: find a way to download the lists of human
#Calculating pearson correlation for each edge
print('Calculating Pearson correlation '+str(utex.buildNamefile(l))+'...')
if vitis: #TODO: manage Pearson Correlation if expansion with GT-001
#listForPearson = [((list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(a)]),(list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(c)]),d) for (a,b,c,d) in l[1:]]
#TODEL_GT-001
dictConvert = {}
for elem in l[1:]:
if elem[0] != 'GT-001':
dictConvert[(list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(elem[0])])] = elem[0]
if elem[2] != 'GT-001':
dictConvert[(list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(elem[2])])] = elem[2]
listForPearson = [((list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(a)]),(list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(c)]),d) for (a,b,c,d) in l[1:] if a != 'GT-001' and c != 'GT-001']
tmp = utex.manageBR(ut.pearsonCorrelation(listForPearson, 'vv_exprdata_2.csv'))
#pearson = [(listBioNameUpdate[u],listBioNameUpdate[v],p) for (u,v,p) in tmp]
#TODEL_GT-001
#pearson = [(listBioNameUpdate[u],listBioNameUpdate[v],p) for (u,v,p) in tmp]+[((list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(a)]),(list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(c)]),1) for (a,b,c,d) in l[1:] if a == 'GT-001' or c == 'GT-001']
pearson = [(dictConvert[[w for w in u.split('<BR>') if w in dictConvert.keys()][0]],dictConvert[[w for w in v.split('<BR>') if w in dictConvert.keys()][0]],p) for (u,v,p) in tmp]+[((list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(a)]),(list(listBioNameUpdate.keys())[list(listBioNameUpdate.values()).index(c)]),1) for (a,b,c,d) in l[1:] if a == 'GT-001' or c == 'GT-001']
pearsonComplete.append(pearson)
else:
listForPearson = [(a,c,d) for (a,b,c,d) in l[1:]]
pearsonComplete.append(listForPearson)
#TODO: calculate pearson correlation for human
print('Pearson Correlation done')
#Draw graph
graphic.printCommonGraph(edgesGraph, pearsonComplete, 1-min_frel, nameDir, autoSaveImg, listBioNameUpdate)
else:
print('ERROR: wrong format')
utex.printInfo()