def all_siblings(name = default_name, reset = 0):
donp = True
hardcopy = True
if not reset:
out, sxs = nw.rn2(default_name,
np = donp, hardcopy = hardcopy)
if reset or not sxs:
trgs, tfs = nu.parse_net(reset=mod(reset,2))
na = nu.net_affinity(reset = mod(reset,2))
na_thr = greater(na, min_thr)
nrms = sqrt(sum(power(na_thr,2),1))[:,newaxis]
nrms[equal(nrms,0)] = 1
nnn = array(na_thr,float)/nrms
gg = dot(nnn,nnn.T)
sibs = array(greater(gg, sib_thr), bool)
nw.wn2(default_name, sibs,
np = donp,
hardcopy = hardcopy)
out = sibs
return out
def test_subs(nmax = 2000,reset = 0, name = default_name):
if not reset:
out,sxs = nw.rn2(name)
if not sxs: raise Exception()
tgls,tfls = out
else:
tfls = []
tgls = []
for cidx in range(nmax):
tgs, tfs = get_subs(cidx)
tgls.append(len(tgs))
tfls.append(len(tfs))
nw.wn2(name, (tgls,tfls))
return tgls, tfls
def net_cluster_ggsvdV(k = k,reset = 0):
hardcopy = True
try:
if reset: raise Exception('compute')
out,sxs = nw.rn2(name, hardcopy = hardcopy)
if not sxs: raise Exception()
except Exception as e:
if e.args[0] != 'compute': raise Exception()
nw.claim_reset()
gg = ns.net_ggsvd_V().T
kmeans = mlpy.Kmeans(k)
clustered = kmeans.compute(gg[0:maxgenes,:])
means = kmeans.means
nw.wn2(name, (clustered, means) ,hardcopy = hardcopy)
return out
def net_cluster_genes_by_tf(k= k, reset = 0):
hardcopy = True
try:
if reset: raise Exception('compute')
out,sxs = nw.rn2(name, hardcopy = hardcopy)
if not sxs: raise Exception()
except Exception as e:
if e.args[0] != 'compute': raise Exception()
nw.claim_reset()
gg = nu.net_affinity(reset = mod(reset,2))
kmeans = mlpy.Kmeans(k)
clustered = kmeans.compute(gg[0:maxgenes,:])
means = kmeans.means
out = (clustered,means)
nw.wn2(name, (clustered, means) ,hardcopy = hardcopy)
return out
def sib_lists(name = default_name, reset = 0, punish = True):
if not reset:
out, sxs = nw.rn2(name = name, hardcopy = True, np = True)
if not sxs: raise Exception()
else:
if punish:
sibs = all_siblings_punish_hubs(name, reset = mod(reset,2))
else:
sibs = all_siblings(name, reset = mod(reset,2))
counts = zeros(len(sibs))
for i in sibs:
counts[i] = len(nonzero(i)[0])
f = plt.figure(2)
f.clear()
ax = f.add_subplot(111)
ax.plot(sort(counts))
nw.wn2(name , sibs, hardcopy = True, np = True)
out = sibs
return out
