"35": "Artery - Tibial",
"34": "Brain - Cortex",
"33": "Heart - Left Ventricle",
"32": "Brain - Hippocampus",
"50": "Brain - Substantia nigra",
}
graph = nx.read_gpickle(sys.argv[1])
graph_nodes = graph.nodes()
start_nodes = load_input_list(sys.argv[2])
graph_choice = "intact"
print "starting", len(start_nodes)
fishertest.load(
list(set(start_nodes)),
0.05,
["C", "P", "F", "R", "K", "O", "KDr", "KDi", "DB", "Or", "VH"],
graph_choice,
path_def="results_old/",
single="0",
)
nodes = list(set(start_nodes).intersection(set(graph_nodes)))
folder = "tissue_expr_norm/"
for i in range(0, 48, 1):
print str(i) + " " + pa_basal[i]
val = raw_input()
if "," not in val:
val = map(int, str(val).split())
else:
print val
val = map(int, val.split(","))
def mcn(nodes, graph_nodes, graph, expression, graph_choice, start_nodes):
graph_go_term, fisher_one_occurence = load_go_term(graph_choice)
path = {}
removable = []
path_prob = {}
path_value = {}
path_count = {}
seek = {}
nodes_ic_value = {}
nodes_ic_path = {}
combination = list(itertools.combinations((list(set(graph_nodes).intersection(set(nodes)))), 2))
for i in nodes:
seek[i] = {}
for i in combination:
path_value[i] = 0.0
path_count[i] = {}
nodes_ic_value[i] = 10
nodes_ic_path[i] = []
f1 = open(
"../../web2py_test_new_version/applications/magneto/data/" + graph_choice + "/path/index/" + i[0] + ".txt",
"r",
)
seq = f1.readline()
while seq != "":
seq = seq.strip().split("\t")
if seq[0] == i[1]:
seek[i[0]][seq[0]] = int(seq[1])
seq = f1.readline()
to_remove = []
for i in seek:
if len(seek[i]) > 0:
for j in seek[i]:
f1 = open(
"../../web2py_test_new_version/applications/magneto/data/" + graph_choice + "/path/" + i + ".txt"
)
f1.seek(seek[i][j])
seq = f1.readline()
key_start = seq.split("|")[0][1:].strip()
key_end = seq.split("|")[1].strip()
key = (key_start, key_end)
path[key] = []
seq = f1.readline()
count = 0
while seq[0] != ">":
path_seq = seq.strip()
node_list = seq.strip().split()[0:-2]
# print node_list
path[key].append(node_list)
path_coex = []
path_exp = []
for k in range(len(node_list[0:-1])):
if k == 0:
path_coex.append(graph[node_list[k]][node_list[k + 1]]["capacity"])
if k != 0:
path_coex.append(graph[node_list[k]][node_list[k + 1]]["capacity"])
if tissue_expr.has_key(node_list[k]):
path_exp.append(tissue_expr[node_list[k]])
else:
path_exp.append(0.0001)
removable.append(node_list[1:-1])
seq = f1.readline()
if len(path_exp) == 0:
val1 = 1.00000
else:
val1 = scipy.stats.mstats.gmean(path_exp)
val2 = scipy.stats.mstats.gmean(path_coex)
total_prob = math.sqrt(val1 * val2)
path_count[key][total_prob] = node_list
# print key,node_list,total_prob,count
count = count + 1
nodes_ic = []
flow = []
# graph_go_term,fisher_one_occurence
for i in path_count:
if len(path_count[i]) == 1:
nodes_ic_path[i] = path_count[i].values()[0]
# flow.append(path_count[i].values()[0])
else:
count = 0
# max_score=sorted(path_count[i].keys())[::-1][0]
mean = np.mean(path_count[i].keys())
for k in sorted(path_count[i].items(), key=itemgetter(0))[::-1]:
if k[0] >= mean:
temp_C = []
temp_P = []
temp_F = []
temp_R = []
temp_K = []
if count == 0:
flow.append(k[1])
for j in k[1]:
if graph_go_term["P"].has_key(j):
temp_P.extend(graph_go_term["P"][j])
if graph_go_term["K"].has_key(j):
temp_K.extend(graph_go_term["K"][j])
if graph_go_term["R"].has_key(j):
temp_R.extend(graph_go_term["R"][j])
if graph_go_term["C"].has_key(j):
temp_C.extend(graph_go_term["C"][j])
if graph_go_term["F"].has_key(j):
temp_F.extend(graph_go_term["F"][j])
print k[1], len(temp_C) + len(temp_P) + len(temp_R) + len(temp_F) + len(temp_K)
value_p = 0.0
value_c = 0.0
value_f = 0.0
value_r = 0.0
value_k = 0.0
for j in set(temp_P):
if fisher_one_occurence["P"].has_key(j):
value_p = value_p + fisher_one_occurence["P"][j]
for j in set(temp_C):
if fisher_one_occurence["C"].has_key(j):
value_c = value_c + fisher_one_occurence["C"][j]
for j in set(temp_R):
if fisher_one_occurence["R"].has_key(j):
value_r = value_r + fisher_one_occurence["R"][j]
for j in set(temp_F):
if fisher_one_occurence["F"].has_key(j):
value_f = value_f + fisher_one_occurence["F"][j]
for j in set(temp_K):
if fisher_one_occurence["K"].has_key(j):
value_k = value_k + fisher_one_occurence["K"][j]
# print k[1],value_p+value_c+value_r+value_f+value_k,float(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K))
go_value = (value_p + value_c + value_r + value_f + value_k) / float(
len(temp_C) + len(temp_P) + len(temp_R) + len(temp_F) + len(temp_K)
)
# print k[1],value_p+value_c+value_r+value_f+value_k,float(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K)),go_value
# go_value=(value_p+value_c+value_r+value_f)/float(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F))
if go_value < nodes_ic_value[i]:
nodes_ic_value[i] = go_value
f5.write(
"\t".join(k[1])
+ "\t"
+ str(go_value)
+ "\t"
+ str(value_p + value_c + value_r + value_f + value_k)
+ "\t"
+ str(len(temp_C) + len(temp_P) + len(temp_R) + len(temp_F) + len(temp_K))
+ "\n"
)
nodes_ic_path[i] = k[1]
# print i,k[1],go_value
# print i,k[1],k[0],go_value,float(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F))
count = count + 1
# for i in nodes_ic_path:
# print i,nodes_ic_path[i],nodes_ic_value[i]
nodes_ic = list(set(sum(nodes_ic_path.values(), []) + start_nodes))
flow = list(set(sum(flow, [])))
print "nodes_ic", len(nodes_ic)
print "flow", len(flow)
fishertest.load(
nodes_ic,
0.05,
["C", "P", "F", "R", "K", "O", "KDr", "KDi", "DB", "Or", "VH"],
graph_choice,
path_def="ic/",
single=sys.argv[3],
)
def mcn(nodes,graph_nodes,graph,expression,graph_choice,start_nodes,tissue,maxmin):
graph_go_term,fisher_one_occurence,fisher_one_occurence2,trends,tissue_protein_prob=load_go_term(graph_choice,tissue)
path={}
removable=[]
path_prob={}
path_value={}
path_count={}
seek={}
nodes_ic_value={}
nodes_ic_path={}
combination=list(itertools.combinations((list(set(graph_nodes).intersection(set(nodes)))),2))
for i in nodes:
seek[i]={}
for i in combination:
path_value[i]=0.0
path_count[i]={}
if maxmin=="0":
nodes_ic_value[i]=100000
else:
nodes_ic_value[i]=-1
nodes_ic_path[i]=[]
f1=open("../../web2py_test_new_version/applications/magneto/data/"+graph_choice+"/path/index/"+i[0]+".txt","r")
seq=f1.readline()
while(seq!=""):
seq= seq.strip().split("\t")
if seq[0]==i[1]:
seek[i[0]][seq[0]]=int(seq[1])
seq=f1.readline()
to_remove=[]
minimum=min(tissue_expr.values())
for i in seek:
if len(seek[i])>0:
for j in seek[i]:
f1=open("../../web2py_test_new_version/applications/magneto/data/"+graph_choice+"/path/"+i+".txt")
f1.seek(seek[i][j])
seq=f1.readline()
key_start=seq.split("|")[0][1:].strip()
key_end=seq.split("|")[1].strip()
key=(key_start,key_end)
path[key]=[]
seq=f1.readline()
count=0
while(seq[0]!=">"):
path_seq=seq.strip()
node_list=seq.strip().split()[0:-2]
#print node_list
path[key].append(node_list)
path_coex=[]
path_exp=[]
expr_coex_prob=0.0
for k in range(len(node_list[0:-1])):
if k==0:
path_coex.append(graph[node_list[k]][node_list[k+1]]["coex"][1])
if k!=0:
path_coex.append(graph[node_list[k]][node_list[k+1]]["coex"][1])
if tissue_expr.has_key(node_list[k]):
path_exp.append(tissue_expr[node_list[k]])
else:
path_exp.append(minimum)
removable.append(node_list[1:-1])
seq=f1.readline()
if len(path_exp)==0:
val1=1.00000
else:
val1=scipy.stats.mstats.gmean(path_exp)
val2=scipy.stats.mstats.gmean(path_coex)
total_prob=math.sqrt(val1*val2)
path_count[key][total_prob]=node_list
count=count+1
nodes_ic=[]
for i in path_count:
if len(path_count[i])==1:
nodes_ic_path[i]=path_count[i].values()[0]
#flow.append(path_count[i].values()[0])
else:
count=0
#max_score=sorted(path_count[i].keys())[::-1][0]
mean=np.mean(path_count[i].keys())
sorted_path_count=sorted(path_count[i].items(), key=itemgetter(0))[::-1]
for k in sorted_path_count:
if k[0]>=mean:
temp=[]
temp_C=[]
temp_P=[]
temp_F=[]
temp_R=[]
temp_K=[]
temp_prob=[]
for j in k[1]:
temp_prob.append(tissue_protein_prob[j])
if graph_go_term["P"].has_key(j):
temp_P.extend(graph_go_term["P"][j])
if graph_go_term["K"].has_key(j):
temp_K.extend(graph_go_term["K"][j])
if graph_go_term["R"].has_key(j):
temp_R.extend(graph_go_term["R"][j])
if graph_go_term["C"].has_key(j):
temp_C.extend(graph_go_term["C"][j])
if graph_go_term["F"].has_key(j):
temp_F.extend(graph_go_term["F"][j])
#print k[1],len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K)
value_p=0.0
value_c=0.0
value_f=0.0
value_r=0.0
value_k=0.0
c=0
for j in set(temp_P):
if fisher_one_occurence["P"].has_key(j):
value_p=value_p+ fisher_one_occurence["P"][j]
#temp.append(fisher_one_occurence["P"][j])
#coex_temp_P.append(fisher_one_occurence["P"][j])
else:
c=c+1
#value_p=value_p+ fisher_one_occurence2["P"][j]
for j in set(temp_C):
if fisher_one_occurence["C"].has_key(j):
value_c=value_c+ fisher_one_occurence["C"][j]
#temp.append(fisher_one_occurence["C"][j])
else:
c=c+1
#value_c=value_c+ fisher_one_occurence2["C"][j]
for j in set(temp_R):
if fisher_one_occurence["R"].has_key(j):
value_r=value_r+ fisher_one_occurence["R"][j]
#temp.append(fisher_one_occurence["R"][j])
else:
c=c+1
#value_r=value_r+ fisher_one_occurence2["R"][j]
for j in set(temp_F):
if fisher_one_occurence["F"].has_key(j):
value_f=value_f+ fisher_one_occurence["F"][j]
#temp.append(fisher_one_occurence["F"][j])
else:
c=c+1
#value_r=value_r+ fisher_one_occurence2["F"][j]
for j in set(temp_K):
if fisher_one_occurence["K"].has_key(j):
value_k=value_k+ fisher_one_occurence["K"][j]
#temp.append(fisher_one_occurence["K"][j])
else:
c=c+1
#print i,k,len(temp)
#n, (smin, smax), sm, sv, ss, sk= scipy.stats.describe(temp)
#print k[1],len(temp),sm,sv,trends[0][1],trends[0][2],sm-float(trends[0][1]),sv-float(trends[0][2])
#go_value=(value_p*value_c*value_r*value_f*value_k)*float(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K))
go_value=(value_p+value_c+value_r+value_f+value_k)/float(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K))
#print k[1],len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K),go_value,scipy.stats.mstats.gmean(temp_prob)
#go_value=scipy.stats.mstats.gmean(temp_prob)
#go_value=np.mean(temp_prob)
#go_value=math.fabs(sm-float(trends[0][1]))+math.fabs(sv-float(trends[0][2]))
f5.write("\t".join(k[1])+"\t"+str(go_value)+"\t"+str(np.mean(temp))+"\t"+str(value_p+value_c+value_r+value_f+value_k)+"\t"+str(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K))+"\n")
if maxmin=="0":
if go_value<nodes_ic_value[i]:
nodes_ic_value[i]=go_value
f5.write(">"+"\t".join(k[1])+"\t"+str(go_value)+"\t"+str(np.mean(temp))+"\t"+str(value_p+value_c+value_r+value_f+value_k)+"\t"+str(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K))+"\n")
nodes_ic_path[i]=k[1]
else:
if go_value>nodes_ic_value[i]:
nodes_ic_value[i]=go_value
f5.write(">"+"\t".join(k[1])+"\t"+str(go_value)+"\t"+str(np.mean(temp))+"\t"+str(value_p+value_c+value_r+value_f+value_k)+"\t"+str(len(temp_C)+len(temp_P)+len(temp_R)+len(temp_F)+len(temp_K))+"\n")
nodes_ic_path[i]=k[1]
count=count+1
for i in nodes_ic_path:
f6.write(i[0]+"\t"+i[1]+"\t"+"\t".join(nodes_ic_path[i])+"\n")
# print i,nodes_ic_path[i],nodes_ic_value[i]
nodes_ic=list(set(sum(nodes_ic_path.values(),[])+start_nodes))
#flow=list(set(sum(flow,[])+start_nodes))
print "nodes_ic",len(nodes_ic)
#print "flow",len(set(flow))
fishertest.load(nodes_ic,0.05,["C","P","F","R","K","O","KDr","KDi","DB","Or","VH"],graph_choice,path_def="ic3/",single=sys.argv[3])
