def spoof_maxent_motifs(motif, num_motifs, verbose=False, A=4):
N = len(motif)
L = len(motif[0])
des_ic = motif_ic(motif, A=A)
if verbose:
print "N: {} L: {} des_ic: {}".format(N, L, des_ic)
return maxent_motifs(N, L, des_ic, num_motifs, verbose=verbose, A=A)
def spoof_motif_cftp(motif, num_motifs=10, trials=1, sigma=None,Ne_tol=10**-2,verbose=False):
n = len(motif)
L = len(motif[0])
copies = 10*n
if sigma is None: sigma = sigma_from_matrix(pssm_from_motif(motif,pc=1))
print "sigma:", sigma
bio_ic = motif_ic(motif)
matrix = sample_matrix(L, sigma)
mu = approx_mu(matrix, copies=10*n, G=5*10**6)
print "mu:", mu
def f(Ne):
motifs = [sample_motif_cftp(matrix, mu, Ne, n, verbose=verbose)
for i in trange(trials)]
return mean(map(motif_ic,motifs)) - bio_ic
# lb = 1
# ub = 10
# while f(ub) < 0:
# ub *= 2
# print ub
x0s = [2,10]#(lb + ub)/2.0
# print "choosing starting seed for Ne"
# fs = map(lambda x:abs(f(x)),x0s)
# print "starting values:",x0s,fs
# x0 = x0s[argmin(fs)]
# print "chose:",x0
# Ne = bisect_interval_noisy_ref(f,x0,lb=1,verbose=True)
Ne = log_regress_spec2(f,x0s,tol=Ne_tol)
print "Ne:",Ne
return [sample_motif_cftp(matrix, mu, Ne, n) for _ in trange(num_motifs)]
def spoof_maxent_motif(motif, verbose=False, A=4):
N = len(motif)
L = len(motif[0])
des_ic = motif_ic(motif)
if verbose:
print "n: {} L: {} des_ic: {}".format(n, L, des_ic)
return maxent_motif(N, L, des_ic, verbose=verbose, A=A)
def spoof_maxent_motifs(motif,num_motifs,verbose=False, A=4):
N = len(motif)
L = len(motif[0])
des_ic = motif_ic(motif, A=A)
if verbose:
print "N: {} L: {} des_ic: {}".format(N,L,des_ic)
return maxent_motifs(N,L,des_ic,num_motifs,verbose=verbose, A=A)
def spoof_maxent_motif(motif,verbose=False, A=4):
N = len(motif)
L = len(motif[0])
des_ic = motif_ic(motif)
if verbose:
print "n: {} L: {} des_ic: {}".format(n,L,des_ic)
return maxent_motif(N,L,des_ic,verbose=verbose, A=A)
def spoof_uniform_motifs(motif, num_motifs, epsilon=0.1, verbose=False):
N, L = len(motif), len(motif[0])
desired_ic = motif_ic(motif)
if verbose: print "starting spoof motifs uniform with:", N, L, desired_ic
return uniform_motifs(N,
L,
desired_ic,
num_motifs,
epsilon,
verbose=verbose)
def test_spoof_motif_cftp():
motif = [
'CGGTGAACTA', 'CGGTGTGCGA', 'CGCTGTGCTG', 'CGGGATGCAA', 'CACGCTACGA',
'CGCTATGCTA', 'CGGTTGGCTA', 'CGGCGTGCTA', 'CGGTATATTG', 'CGGGTTGCGA'
]
given_ic = motif_ic(motif) # ~ 9.05 bits
motifs = spoof_motif_cftp(motif, num_motifs=1000)
spoof_ics = map(motif_ic, motifs)
lb, ub = mean_ci(spoof_ics)
print lb, given_ic, ub
assert_less_equal(lb - 1, given_ic)
assert_less_equal(given_ic, ub + 1)
def test_spoof_motif_cftp():
motif = ['CGGTGAACTA',
'CGGTGTGCGA',
'CGCTGTGCTG',
'CGGGATGCAA',
'CACGCTACGA',
'CGCTATGCTA',
'CGGTTGGCTA',
'CGGCGTGCTA',
'CGGTATATTG',
'CGGGTTGCGA']
given_ic = motif_ic(motif) # ~ 9.05 bits
motifs = spoof_motif_cftp(motif, num_motifs=1000)
spoof_ics = map(motif_ic, motifs)
lb, ub = mean_ci(spoof_ics)
print lb, given_ic, ub
assert_less_equal(lb - 1, given_ic)
assert_less_equal(given_ic, ub + 1)
def spoof_motif_cftp(motif,
num_motifs=10,
trials=1,
sigma=None,
Ne_tol=10**-2,
verbose=False):
n = len(motif)
L = len(motif[0])
copies = 10 * n
if sigma is None: sigma = sigma_from_matrix(pssm_from_motif(motif, pc=1))
print "sigma:", sigma
bio_ic = motif_ic(motif)
matrix = sample_matrix(L, sigma)
mu = approx_mu(matrix, copies=10 * n, G=5 * 10**6)
print "mu:", mu
def f(Ne):
motifs = [
sample_motif_cftp(matrix, mu, Ne, n, verbose=verbose)
for i in trange(trials)
]
return mean(map(motif_ic, motifs)) - bio_ic
# lb = 1
# ub = 10
# while f(ub) < 0:
# ub *= 2
# print ub
x0s = [2, 10] #(lb + ub)/2.0
# print "choosing starting seed for Ne"
# fs = map(lambda x:abs(f(x)),x0s)
# print "starting values:",x0s,fs
# x0 = x0s[argmin(fs)]
# print "chose:",x0
# Ne = bisect_interval_noisy_ref(f,x0,lb=1,verbose=True)
Ne = log_regress_spec2(f, x0s, tol=Ne_tol)
print "Ne:", Ne
return [sample_motif_cftp(matrix, mu, Ne, n) for _ in trange(num_motifs)]
def spoof_uniform_motif(motif, epsilon=0.1, verbose=False):
N, L = len(motif), len(motif[0])
desired_ic = motif_ic(motif)
if verbose: print "starting spoof motifs uniform with:",n,L,desired_ic
return uniform_motifs(N, L, desired_ic, num_motifs, epsilon, verbose=verbose)[0]
def dQ(motif):
return exp(beta*motif_ic(motif) + log_M)
def dQhat(motif):
return exp(beta*motif_ic(motif))
def dQ(motif):
return exp(beta * motif_ic(motif) + log_M)
def dQhat(motif):
return exp(beta * motif_ic(motif))
