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 sample_motif_with_ic(n,L):
matrix = sample_matrix(L,sigma=1)
ringer_site = "".join(["ACGT"[argmin(col)] for col in matrix])
mu = approximate_mu(matrix,10*n,G=5*10**6)
Nes = range(2,10)
trials = 10
motifs = [[sample_motif_mh(matrix, mu, Ne, n)
for t in range(trials)] for Ne in tqdm(Nes)]
def sample_site_study(trials=1000):
sigmas = np.linspace(0,10,100)
Nes = np.linspace(2,10,100)
mus = np.linspace(-10,10,100)
L = 10
results = {}
for trial in trange(trials):
sigma = random.choice(sigmas)
Ne = random.choice(Nes)
mu = random.choice(mus)
matrix = sample_matrix(L,sigma)
ringer_site = ringer_motif(matrix,1)[0]
t0 = time.time()
sites = [sample_site_bf(matrix, mu, Ne, ringer_site) for i in range(10)]
t = time.time() - t0
results[(sigma, Ne, mu)] = t
return results
def spoof_motif_ar(motif, num_motifs=10, trials=1, sigma=None,Ne_tol=10**-4):
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_ar(matrix, mu, Ne, n)
for i in trange(trials)]
return mean(map(motif_ic,motifs)) - bio_ic
x0 = 2
print "Ne guess:", x0
Ne = bisect_interval_noisy(f,x0=x0,iterations=100,lb=1, verbose=False,w=0.5)
print "Ne:",Ne
return [sample_motif_ar(matrix, mu, Ne, n) for _ in trange(num_motifs)]
def site_sampling_methods_study(n=50, num_motifs=10, plot=True):
"""validate that the three proposed sampling methods:
brute force
rejection sampling
metropolis hastings
do in fact sample from the same distribution
"""
L = 10
sigma = 1
matrix = sample_matrix(L, sigma)
Ne = 5
mu = -10
print "bf"
t0 = time.time()
bf_motifs = [sample_motif_bf(matrix, mu, Ne, n,verbose=True)
for i in trange(num_motifs)]
bf_time = time.time() - t0
print "ar"
t0 = time.time()
ar_motifs = [sample_motif_ar(matrix, mu, Ne, n)
for i in range(num_motifs)]
ar_time = time.time() - t0
print "mh"
t0 = time.time()
mh_motifs = [sample_motif_mh(matrix, mu, Ne, n)
for i in range(num_motifs)]
mh_time = time.time() - t0
icss = mmap(motif_ic,[bf_motifs, ar_motifs, mh_motifs])
print "ics:", map(mean_ci, icss)
print "time per motif:", [t/num_motifs
for t in [bf_time, ar_time, mh_time]]
if plot:
plt.boxplot(icss)
for xs, ys in choose2(icss):
print mannwhitneyu(xs,ys)
def mh_cftp_comparison():
matrix = sample_matrix(10,1)
mu = -10
Ne = 5
mh_ics = [motif_ic(sample_motif_mh(matrix, mu, Ne, 50)) for i in trange(100)]
cftp_ics = [motif_ic(sample_motif_cftp(matrix, mu, Ne, 50)) for i in trange(100)]
def f(sigma, Ne):
matrix = sample_matrix(L, sigma)
mu = approx_mu(matrix, 10*n)
return motif_ic(sample_motif_cftp(matrix, mu, Ne, n))
def f(sigma, Ne):
matrix = sample_matrix(L, sigma)
mu = approx_mu(matrix, 10*n)
return motif_ic(sample_motif_ar(matrix, mu, Ne, n, modulus=10**5))