def make_old_pseknc_vector(sequence_list, lamada, w, k, phyche_value, theta_type=1):
"""Generate the pseknc vector."""
kmer = make_kmer_list(k, ALPHABET)
vector = []
for sequence in sequence_list:
if len(sequence) < k or lamada + k > len(sequence):
error_info = "Sorry, the sequence length must be larger than " + str(lamada + k)
sys.stderr.write(error_info)
sys.exit(0)
# Get the nucleotide frequency in the DNA sequence.
fre_list = [frequency(sequence, str(key)) for key in kmer]
fre_sum = float(sum(fre_list))
# Get the normalized occurrence frequency of nucleotide in the DNA sequence.
fre_list = [e / fre_sum for e in fre_list]
# Get the theta_list according the Equation 5.
if 1 == theta_type:
theta_list = get_parallel_factor_psednc(lamada, sequence, phyche_value)
elif 2 == theta_type:
theta_list = get_series_factor(k, lamada, sequence, phyche_value)
theta_sum = sum(theta_list)
# Generate the vector according the Equation 9.
denominator = 1 + w * theta_sum
temp_vec = [round(f / denominator, 3) for f in fre_list]
for theta in theta_list:
temp_vec.append(round(w * theta / denominator, 4))
vector.append(temp_vec)
return vector
def make_pseknc_vector(sequence_list,
phyche_value,
k=2,
w=0.05,
lamada=1,
alphabet=index_list.DNA,
theta_type=1):
"""Generate the pseknc vector."""
kmer = make_kmer_list(k, alphabet)
vector = []
for sequence in sequence_list:
if len(sequence) < k or lamada + k > len(sequence):
error_info = "Sorry, the sequence length must be larger than " + str(
lamada + k)
sys.stderr.write(error_info)
sys.exit(0)
# Get the nucleotide frequency in the DNA sequence.
fre_list = [frequency(sequence, str(key)) for key in kmer]
fre_sum = float(sum(fre_list))
# Get the normalized occurrence frequency of nucleotide in the DNA sequence.
fre_list = [e / fre_sum for e in fre_list]
# Get the theta_list.
if 1 == theta_type:
theta_list = get_parallel_factor(k, lamada, sequence, phyche_value,
alphabet)
elif 2 == theta_type:
theta_list = get_series_factor(k, lamada, sequence, phyche_value,
alphabet)
elif 3 == theta_type:
theta_list = get_parallel_factor(k=2,
lamada=lamada,
sequence=sequence,
phyche_value=phyche_value,
alphabet=alphabet)
theta_sum = sum(theta_list)
# Generate the vector according the Equation 9.
denominator = 1 + w * theta_sum
temp_vec = [round(f / denominator, 8) for f in fre_list]
for theta in theta_list:
temp_vec.append(round(w * theta / denominator, 8))
vector.append(temp_vec)
return vector
def make_kmer_vector(k, alphabet, filename, revcomp=False):
"""Generate kmer vector."""
with open(filename) as f:
seq_list = get_data(f, alphabet=alphabet)
if revcomp and re.search(r'[^acgtACGT]',
''.join(alphabet)) is not None:
sys.exit("Error, Only DNA sequence can be reverse compliment.")
vector = []
kmer_list = make_kmer_list(k, alphabet)
for seq in seq_list:
count_sum = 0
# Generate the kmer frequency dict.
kmer_count = {}
for kmer in kmer_list:
temp_count = frequency(seq, kmer)
if not revcomp:
if kmer not in kmer_count:
kmer_count[kmer] = 0
kmer_count[kmer] += temp_count
else:
rev_kmer = find_revcomp(kmer, {})
if kmer <= rev_kmer:
if kmer not in kmer_count:
kmer_count[kmer] = 0
kmer_count[kmer] += temp_count
else:
if rev_kmer not in kmer_count:
kmer_count[rev_kmer] = 0
kmer_count[rev_kmer] += temp_count
count_sum += temp_count
# Normalize.
if not revcomp:
count_vec = [kmer_count[kmer] for kmer in kmer_list]
else:
revc_kmer_list = make_revcomp_kmer_list(kmer_list)
count_vec = [kmer_count[kmer] for kmer in revc_kmer_list]
count_vec = [round(float(e) / count_sum, 8) for e in count_vec]
vector.append(count_vec)
return vector
def make_kmer_vector(k, alphabet, filename, revcomp=False):
"""Generate kmer vector."""
with open(filename) as f:
seq_list = get_data(f, alphabet=alphabet)
if revcomp and re.search(r'[^acgtACGT]', ''.join(alphabet)) is not None:
sys.exit("Error, Only DNA sequence can be reverse compliment.")
vector = []
kmer_list = make_kmer_list(k, alphabet)
for seq in seq_list:
count_sum = 0
# Generate the kmer frequency dict.
kmer_count = {}
for kmer in kmer_list:
temp_count = frequency(seq, kmer)
if not revcomp:
if kmer not in kmer_count:
kmer_count[kmer] = 0
kmer_count[kmer] += temp_count
else:
rev_kmer = find_revcomp(kmer, {})
if kmer <= rev_kmer:
if kmer not in kmer_count:
kmer_count[kmer] = 0
kmer_count[kmer] += temp_count
else:
if rev_kmer not in kmer_count:
kmer_count[rev_kmer] = 0
kmer_count[rev_kmer] += temp_count
count_sum += temp_count
# Normalize.
if not revcomp:
count_vec = [kmer_count[kmer] for kmer in kmer_list]
else:
revc_kmer_list = make_revcomp_kmer_list(kmer_list)
count_vec = [kmer_count[kmer] for kmer in revc_kmer_list]
count_vec = [round(float(e)/count_sum, 8) for e in count_vec]
vector.append(count_vec)
return vector
def estimations_from_predictions(predictions, annotations, tuning, config):
e_list = []
for prediction, annotation in zip(predictions, annotations):
string = annotation.string
fret = annotation.fret
normal_freq = util.frequency(fret, tuning.tuning[string - 1])
estimations = init_estimations_from_normal(normal_freq, tuning, config)
string = 1
for confidence in prediction:
for estimation in estimations:
if estimation.string == string:
estimation.set_weight(confidence)
string += 1
# init estimation object with weight = 0
e = make_estimation()
# find estimation with max weight
for estimation in estimations:
if estimation.weight > e.weight:
e = estimation
e_list.append(e)
return e_list
def make_kmer_vector_ID(seq_list, kmer_list, rev_kmer_list, k, upto, revcomp, normalize):
"""Generate kmer vector."""
# Generate the alphabet index.
if upto:
index = make_index_upto_k(k)
sum = [0] * k
len_k = k
else:
index = make_index(k)
sum = [0]
len_k = 1
vector = []
for seq in seq_list:
kmer_count = {}
# Generate the kmer frequency vector.
for i in range(len_k):
sum[i] = 0
for j in range(index[i], index[i + 1]):
kmer = kmer_list[j]
temp_count = frequency(seq, kmer)
# print temp_count
if revcomp:
rev_kmer = find_revcomp(kmer, {})
if kmer <= rev_kmer:
if kmer not in kmer_count:
kmer_count[kmer] = 0
kmer_count[kmer] += temp_count
else:
if rev_kmer not in kmer_count:
kmer_count[rev_kmer] = 0
kmer_count[rev_kmer] += temp_count
else:
if kmer not in kmer_count:
kmer_count[kmer] = 0
kmer_count[kmer] += temp_count
sum[i] += temp_count
# Store the kmer frequency vector.
if revcomp:
temp_vec = [kmer_count[kmer] for kmer in rev_kmer_list]
else:
temp_vec = [kmer_count[kmer] for kmer in kmer_list]
# Normalize.
if normalize:
i = 0
if not upto:
temp_vec = [round(float(e)/sum[i], 3) for e in temp_vec]
if upto:
if revcomp:
upto_index = make_index_upto_k_revcomp(k)
else:
upto_index = make_index_upto_k(k)
j = 0
for e in temp_vec:
if j >= upto_index[i + 1]:
i += 1
temp_vec[j] = round(float(e) / sum[i], 3)
j += 1
vector.append(temp_vec)
# if 0 != len(rev_kmer_list):
# print "The kmer is", rev_kmer_list
# else:
# print "The kmer is", kmer_list
return vector
#if not defined warp[1] set to None
alpha = [
8.037098164766618, 9.2494445061479, 11.983630862563617, 8.418011746246092,
10.100367601431355, 9.271272689547438, 8.361221354294603, 200,
9.109799699070852, 9.919370757465913
]
beta = [
27.971242258713225, 13.895897768035837, 29.979076350711615,
18.23346507541101, 17.20632120434984, 14.5832818156608, 16.282666801491864,
200, 17.01010654043829, 14.973403850861265
]
theta = [
7.332827665540709, 7.693271996617378, 6.409582916757133, 6.925888943127349,
5.755089466047173, 7.137183585824163, 7.4507863470554625, 200,
5.113392670335407, 6.688337149006028
]
delta = [
1.6747542908272948, 0.792628712689756, 0.9962083257186725,
1.1048836673314748, 1.23381736706717, 1.025315344646628,
1.2172809260762698, 1.542627872299756, 1.2572786093425488,
1.1197264326339946
]
f = util.frequency()
im = binary.bin_image((50, 50), f, '0123', 3, warp=[None, None])
for i, j, k, r in zip(alpha, beta, theta,
delta): # for each time point in the data
f.store_signal(i, j, k, r)
# x = f.store_signal(i,j,k,r)
image = im.create_image()
def make_kmer_vector(seq_list, kmer_list, rev_kmer_list, k, upto, revcomp, normalize):
"""Generate kmer vector."""
# Generate the alphabet index.
if upto:
index = make_index_upto_k(k)
sum = [0] * k
len_k = k
else:
index = make_index(k)
sum = [0]
len_k = 1
vector = []
for seq in seq_list:
kmer_count = {}
# Generate the kmer frequency vector.
for i in range(len_k):
sum[i] = 0
for j in range(index[i], index[i + 1]):
kmer = kmer_list[j]
temp_count = frequency(seq, kmer)
# print temp_count
if revcomp:
rev_kmer = find_revcomp(kmer, {})
if kmer <= rev_kmer:
if kmer not in kmer_count:
kmer_count[kmer] = 0
kmer_count[kmer] += temp_count
else:
if rev_kmer not in kmer_count:
kmer_count[rev_kmer] = 0
kmer_count[rev_kmer] += temp_count
else:
if kmer not in kmer_count:
kmer_count[kmer] = 0
kmer_count[kmer] += temp_count
sum[i] += temp_count
# Store the kmer frequency vector.
if revcomp:
temp_vec = [kmer_count[kmer] for kmer in rev_kmer_list]
else:
temp_vec = [kmer_count[kmer] for kmer in kmer_list]
# Normalize.
if normalize:
i = 0
if not upto:
temp_vec = [round(float(e)/sum[i], 3) for e in temp_vec]
if upto:
if revcomp:
upto_index = make_index_upto_k_revcomp(k)
else:
upto_index = make_index_upto_k(k)
j = 0
for e in temp_vec:
if j >= upto_index[i + 1]:
i += 1
temp_vec[j] = round(float(e) / sum[i], 3)
j += 1
vector.append(temp_vec)
# if 0 != len(rev_kmer_list):
# print "The kmer is", rev_kmer_list
# else:
# print "The kmer is", kmer_list
return vector
