def notchOld():
cys = CyNetwork()
filename = '/Users/vikash/Documents/MATLAB/MetSignal/REACTOME_SBML2/Signaling_by_EGFR.xml'
species, reactions_info = parse_xml_reactome(filename)
cofactorsID = [
'AMP [cytosol]', 'Pi [nucleoplasm]', 'Ca2+ [cytosol]', 'H2O [cytosol]',
'GDP [cytosol]', 'ATP [cytosol]', 'Ub [cytosol]', 'ADP [nucleoplasm]',
'ADP [cytosol]', 'Pi [cytosol', 'ATP [nucleoplasm]', 'GTP [cytosol]'
]
out = open('egfr.txt', 'w')
# species dictionary
new_species = {}
for k, v in species.items():
new_species[v[0]] = v[0].split('[')[0]
print v[0]
out.write("%s\n" % v[0])
for k, v in reactions_info.items():
new_species[k] = ''
import pdb
pdb.set_trace()
rInfo, labels, newIds = regenReaction(reactions_info, new_species,
cofactorsID)
node_list, edge_list = cys.createNodeEdgeCofactor(rInfo, labels, newIds)
cys.createCysNet(node_list, edge_list)
def drawNet(netFile,rxnFile):
rxnList=read_1col(rxnFile)
temp = pickle.load(open(netFile))
reactions_info=temp[2]
new_rInfo = {}
for k in rxnList:
if (k in (temp[0]['rxns'])) & (k in reactions_info.keys()): # this is just for signaling reactions
E = reactions_info[k]['M']
ind = temp[0]['rxns'].index(k)
act = temp[0]['activators'][ind]
inh = temp[0]['inhibitors'][ind]
if not (act == 'NA'):
acts = act.split('|')
for a in acts:
if a in temp[1].keys():
name = temp[1][a][0];
elif a in temp[0]['rashmiv']:
name = temp[0]['rashmik'][temp[0]['rashmiv'].index(a)]
else:
name = a;
print a
E.append(name)
I = []
if not (inh == 'NA'):
acts = inh.split('|')
for a in acts:
if a in temp[1].keys():
name = temp[1][a][0];
elif a in temp[0]['rashmiv']:
name = temp[0]['rashmik'][temp[0]['rashmiv'].index(a)]
else:
name = a;
print a
I.append(name)
new_rInfo[k] = {'S': reactions_info[k]['S'], 'P': reactions_info[k]['P'], 'E': E, 'I': I}
# now we want to create network on cytoscape
# new_rInfo {'reaction_1980109': {'I': [], 'P': ['NICD1:DTX [cytosol]'], 'S': ['CNTN1:NOTCH1:DTX [plasma membrane]'],
#'E': ['gamma-secretase complex [plasma membrane]']},
# species_label species can be repalced
# cofactorsID list of cofactor Ids
cofactorsID=[]
cys = CyNetwork()
species_label={}
rInfo, labels, newIds = regenReaction(new_rInfo, species_label, cofactorsID)
node_list, edge_list = cys.createNodeEdgeCofactor(rInfo, labels, newIds)
cys.createCysNet(node_list, edge_list)
def notchViz():
Info=pickle.load(open('/Users/vikashpandey/Documents/PyCytoscape/VisPathways/notch.pickle'))
for k in Info[0].keys():
print k
import pdb;pdb.set_trace()
cofactorsID = ['AMP [cytosol]', 'Pi [nucleoplasm]', 'Ca2+ [cytosol]', 'H2O [cytosol]', 'GDP [cytosol]',
'ATP [cytosol]', 'Ub [cytosol]', 'ADP [nucleoplasm]', 'ADP [cytosol]', 'Pi [cytosol',
'ATP [nucleoplasm]', 'GTP [cytosol]']
cys = CyNetwork()
rInfo, labels, newIds = regenReaction(Info[0], Info[1],cofactorsID)
node_list, edge_list = cys.createNodeEdgeCofactor(rInfo, labels, newIds)
cys.createCysNet(node_list, edge_list)
def notch1Vizualize(vizFile):
Info = pickle.load(open(vizFile))
# for k in Info[0].keys():
# print k
# import pdb;pdb.set_trace()
# Info[0] : rxn Info
# Info[1]: is label
#import pdb;pdb.set_trace()
cofactorsID = [
'AMP [cytosol]', 'Pi [nucleoplasm]', 'Ca2+ [cytosol]', 'H2O [cytosol]',
'GDP [cytosol]', 'ATP [cytosol]', 'Ub [cytosol]', 'ADP [nucleoplasm]',
'ADP [cytosol]', 'Pi [cytosol', 'ATP [nucleoplasm]', 'GTP [cytosol]'
]
cys = CyNetwork()
rInfo, labels, newIds = regenReaction(Info[0], Info[1], cofactorsID)
node_list, edge_list = cys.createNodeEdgeCofactor(rInfo, labels, newIds)
cys.createCysNet(node_list, edge_list)
def egfViz():
# This is the xml file for the egf network. we can use this to parse any network
fin = '/Users/vikashpandey/Documents/MATLAB/reDoMetSignal/testModels/R-HSA-177929_EGFR.xml'
species, reactions_info = parse_xml_reactome(fin)
# step 2
# we want take information from pickle signaling model because signaling model is more complete
import pickle
sigf = '/Users/vikashpandey/Documents/MATLAB/reDoMetSignal/SignalingAnalysis/PythonData/DataPickle/Signaling.pickle'
data = pickle.load(open(sigf))
species = data[0]
rxnInfo = data[1]
# find reaction and specis info of reduced model from signaling model
rInfo = {}
for rxn in reactions_info.keys():
newRxn = rxn.replace('reaction_', 'R-HSA-')
if newRxn in rxnInfo.keys():
rInfo[newRxn] = rxnInfo[newRxn]
else:
print rxn
#import pdb;pdb.set_trace()
new_rInfo, new_species=collectNewNet(rInfo)
# visualize with new data
cofactorsID = ['AMP [cytosol]', 'Pi [nucleoplasm]', 'Ca2+ [cytosol]', 'H2O [cytosol]', 'GDP [cytosol]',
'ATP [cytosol]', 'Ub [cytosol]', 'ADP [nucleoplasm]', 'ADP [cytosol]', 'Pi [cytosol]',
'ATP [nucleoplasm]', 'GTP [cytosol]']
cys = CyNetwork()
rInfo, labels, newIds = regenReaction(new_rInfo, new_species,cofactorsID)
f= '/Users/vikashpandey/Documents/MATLAB/reDoMetSignal/SignalingAnalysis/PythonData/DataPickle/proteinShortName.pickle'
proteinShortName = pickle.load(open(f))
new_labels={}
for k,v in labels.items():
if k in proteinShortName.keys():
new_labels[k]=proteinShortName[k]
else:
new_labels[k]=v
node_list, edge_list = cys.createNodeEdgeCofactor(rInfo, new_labels,newIds)
cys.createCysNet(node_list, edge_list)
def rxnWiseViz_Maria():
# This function takes input as reactions as a text file
f="/Users/vikash/switchdrive2/RedoConnectMetSig/Data/signaling_viz.txt"
rxns=read_1col(f)
import pickle
sigf='/Users/vikash/Desktop/consign/ExceptMetabolismNew.pickle'
# sigf = '/Users/vikash/switchdrive2/RedoConnectMetSig/Data/Signaling.pickle'
data = pickle.load(open(sigf))
species = data[0]
rxnInfo = data[1]
# find reaction and specis info of reduced model from signaling model
rInfo = {}
for rxn in rxns:
if rxn in rxnInfo.keys():
rInfo[rxn] = rxnInfo[rxn]
else:
print "this is not found in the siganling network= %s"%rxn
# import pdb;
# pdb.set_trace()
# import pdb;pdb.set_trace()
new_rInfo, new_species = collectNewNet(rInfo)
# visualize with new data
cofactorsID = ['AMP [cytosol]', 'Pi [nucleoplasm]', 'Ca2+ [cytosol]', 'H2O [cytosol]', 'GDP [cytosol]',
'ATP [cytosol]', 'Ub [cytosol]', 'ADP [nucleoplasm]', 'ADP [cytosol]', 'Pi [cytosol]',
'ATP [nucleoplasm]', 'GTP [cytosol]']
cys = CyNetwork()
rInfo, labels, newIds = regenReaction(new_rInfo, new_species, cofactorsID)
f = '/Users/vikash/switchdrive2/RedoConnectMetSig/Data/proteinShortName.pickle'
proteinShortName = pickle.load(open(f))
new_labels = {}
for k, v in labels.items():
if k in proteinShortName.keys():
new_labels[k] = proteinShortName[k]
else:
new_labels[k] = v
node_list, edge_list = cys.createNodeEdgeCofactor(rInfo, new_labels, newIds)
cys.createCysNet(node_list, edge_list)
