def f(Ne):
motifs = [
sample_motif_cftp(matrix, mu, Ne, N, verbose=verbose)
for i in trange(trials)
]
return mean(map(lambda m: mean(occupancies(m)), motifs)) - mean_bio_occ
def spoof_motif_cftp_occ(motif, num_motifs=10, trials=1, sigma=None,Ne_tol=10**-2,verbose=False):
"""spoof motifs based on occupancy rather than motif IC"""
N = len(motif)
L = len(motif[0])
copies = 10*N
pssm = pssm_from_motif(motif,pc=1)
if sigma is None: sigma = sigma_from_matrix(pssm)
print "sigma:", sigma
matrix = sample_matrix(L, sigma)
bio_matrix = matrix_from_motif(motif)
mu = approx_mu(matrix, copies=copies, G=5*10**6)
mean_bio_occ = mean(occupancies(motif))
print "mu:", mu
def f(Ne):
motifs = [sample_motif_cftp(matrix, mu, Ne, N, verbose=verbose)
for i in trange(trials)]
return mean(map(lambda m:mean(occupancies(m)), motifs)) - mean_bio_occ
# 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_motif_cftp_occ(motif,
num_motifs=10,
trials=1,
sigma=None,
Ne_tol=10**-2,
verbose=False):
"""spoof motifs based on occupancy rather than motif IC"""
N = len(motif)
L = len(motif[0])
copies = 10 * N
pssm = pssm_from_motif(motif, pc=1)
if sigma is None: sigma = sigma_from_matrix(pssm)
print "sigma:", sigma
matrix = sample_matrix(L, sigma)
bio_matrix = matrix_from_motif(motif)
mu = approx_mu(matrix, copies=copies, G=5 * 10**6)
mean_bio_occ = mean(occupancies(motif))
print "mu:", mu
def f(Ne):
motifs = [
sample_motif_cftp(matrix, mu, Ne, N, verbose=verbose)
for i in trange(trials)
]
return mean(map(lambda m: mean(occupancies(m)), motifs)) - mean_bio_occ
# 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 weighted_regress(xs,ys):
avg_yval = mean(map(abs,ys))
ws = [exp(-abs(y)/avg_yval) for y in ys]
return (polyfit(xs,ys,1,w=ws))
def f(Ne):
motifs = [sample_motif_cftp(matrix, mu, Ne, N, verbose=verbose)
for i in trange(trials)]
return mean(map(lambda m:mean(occupancies(m)), motifs)) - mean_bio_occ
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
def weighted_regress(xs, ys):
avg_yval = mean(map(abs, ys))
ws = [exp(-abs(y) / avg_yval) for y in ys]
return (polyfit(xs, ys, 1, w=ws))
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