def non_normal_cluster_expr(trg_ssnames, tf_ssnames, ctype = False, random_tfs = False):
if random_tfs:
tf_kidxs = nu.net_tf_keyidxs()
r =np.random.random_integers(0,len(tf_kidxs.keys()),len(tf_ssnames))
tf_ssnames = []
print 'Randomizing TFs'
for i in r:
tf_ssnames.append(tf_kidxs.keys()[i])
n = nu.parse_net()
ts = nu.load_TS()
if ctype:
cl = nu.load_CL()
tf_vals = array([ts[k] + cl[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] + cl[k] for k in trg_ssnames]).T
else:
tf_vals = array([ts[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] for k in trg_ssnames]).T
tf_vals -= np.mean(tf_vals,0)[:]
tg_vals -= np.mean(tg_vals,0)[:]
tf_vals /= np.std(tf_vals,0)[:]
tg_vals /= np.std(tg_vals,0)[:]
return [tg_vals, tf_vals]
def normalize_cluster_expr(trg_ssnames, tf_ssnames, ctype = False, random_tfs = False):
if random_tfs:
tf_kidxs = nu.net_tf_keyidxs()
r =np.random.random_integers(0,len(tf_kidxs.keys()),len(tf_ssnames))
tf_ssnames = []
print 'Randomizing TFs'
for i in r:
tf_ssnames.append(tf_kidxs.keys()[i])
n = nu.parse_net()
ts = nu.load_TS()
if ctype:
cl = nu.load_CL()
tf_vals = array([ts[k] + cl[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] + cl[k] for k in trg_ssnames]).T
else:
tf_vals = array([ts[k] for k in tf_ssnames]).T
tg_vals = array([ts[k] for k in trg_ssnames]).T
all_exprs = []
for vstart in [tg_vals, tf_vals]:
vals = vstart
mvals = np.mean(vals,1)
vals -= mvals[:,newaxis]
svals = np.std(vals,1)
if len(nonzero(equal(svals,0))[0]):
raise Exception('only one tf...')
vals /= svals[:,newaxis]
for v in vals.T:
v -= mean(v)
v /= std(v)
all_exprs.append(vals)
#raise Exception()
return all_exprs
