def generateNetworkVisualizationFiles(drugsfile, targetsfile, proteinnodesfile):
fingerprints = util.readDrugData(drugsfile)
[targetSets, ligandSets] = util.readTargetData(targetsfile, fingerprints.keys())
# Unsorted protein nodes data
proteinNodes = np.loadtxt(proteinnodesfile, dtype='string', delimiter=',', skiprows=1)
# Protein nodes data, sorted alphabetically by protein name
sortedProteinNodes = proteinNodes[proteinNodes[:,0].argsort()]
# Initialize data structures
networkData=[]; nameData=[]; indicationData=[]; networkNodes=set()
# Iterate over each node pair to determine if their bootstrap p-value merits an edge connection
for i in range(len(sortedProteinNodes)):
for j in range(i+1, len(sortedProteinNodes)):
proteinA = sortedProteinNodes[i,0]
proteinB = sortedProteinNodes[j,0]
bootstrapPVal = util.calculateBootstrapPValue(100, 214, fingerprints, ligandSets, proteinA, proteinB)
if bootstrapPVal <= 0.05:
networkData.append([proteinA, proteinB]) # Create edge between connected nodes
networkNodes.update([proteinA, proteinB]) # Update set of nodes in network
# Annotate each node with its name and indication
for protein in proteinNodes:
if protein[0] in networkNodes:
nameData.append([protein[0],protein[1]])
indicationData.append([protein[0],protein[2]])
# Output files
np.savetxt('network.sif', np.array(networkData), fmt='%s edge %s')
np.savetxt('name.nodeAttr', np.array(nameData), fmt='%s = %s', header="name", comments='')
np.savetxt('indication.nodeAttr', np.array(indicationData), fmt='%s = %s', header="indication", comments='')
def printBootstrapPValue():
params = initParams()
print util.calculateBootstrapPValue(params['n'], params['r'],
params['fingerprints'], params['ligandSets'],
params['proteinA'], params['proteinB'])
def printBootstrapPValue():
params = initParams()
print util.calculateBootstrapPValue(params['n'], params['r'],
params['fingerprints'],
params['ligandSets'],
params['proteinA'], params['proteinB'])
