def correlation(mat_file_1, mat_file_2):
"""
Draws the plot
"""
blockades_1 = read_mat(mat_file_1)
blockades_1 = sp._fractional_blockades(blockades_1)
blockades_1 = sp._filter_by_duration(blockades_1, 0.5, 20)
blockades_1 = map(lambda b: sp.discretize(sp._trim_flank_noise(b.eventTrace), 20), blockades_1)
blockades_2 = read_mat(mat_file_2)
blockades_2 = sp._fractional_blockades(blockades_2)
blockades_2 = sp._filter_by_duration(blockades_2, 0.5, 20)
blockades_2 = map(lambda b: sp.discretize(sp._trim_flank_noise(b.eventTrace), 20), blockades_2)
self_corr = []
cross_corr = []
for blockade in blockades_1:
block_self = []
for other in blockades_1:
block_self.append(1 - distance.correlation(blockade, other))
block_cross = []
for other in blockades_2:
block_cross.append(1 - distance.correlation(blockade, other))
self_corr.append(np.mean(block_self))
cross_corr.append(np.mean(block_cross))
mean_self = np.median(self_corr)
mean_cross = np.median(cross_corr)
matplotlib.rcParams.update({"font.size": 16})
fig = plt.subplot()
fig.spines["right"].set_visible(False)
fig.spines["top"].set_visible(False)
fig.get_xaxis().tick_bottom()
fig.get_yaxis().tick_left()
fig.set_xlim(-0.6, 0.6)
fig.set_ylim(-0.6, 0.6)
fig.set_xlabel("(H3 tail, H3 tail) correlation")
fig.set_ylabel("(H3 tail, CCL5) correlation")
for y in [-0.4, -0.2, 0, 0.2, 0.4]:
plt.plot((-0.6, 0.6), (y, y), "--",
lw=0.5, color="black")
plt.plot((y, y), (-0.6, 0.6), "--",
lw=0.5, color="black")
plt.plot((-0.6, 0.6), (mean_cross, mean_cross), "--",
lw=1.5, color="red")
plt.plot((mean_self, mean_self), (-0.6, 0.6), "--",
lw=1.5, color="red")
fig.scatter(self_corr, cross_corr, linewidth=0.5, c="dodgerblue",
s=30, edgecolor="blue")
plt.tight_layout()
plt.show()
def get_bias(blockades_file, model_file, cluster_size):
"""
Gets AA-specific bias between the empirical and theoretical signals
"""
WINDOW = 4
blockades = read_mat(blockades_file)
clusters = sp.preprocess_blockades(blockades,
cluster_size=cluster_size,
min_dwell=0.5,
max_dwell=20)
peptide = clusters[0].blockades[0].peptide
blockade_model = load_model(model_file)
errors = defaultdict(list)
model_signal = blockade_model.peptide_signal(peptide)
for cluster in clusters:
discr_signal = sp.discretize(cluster.consensus, len(peptide))
flanked_peptide = ("-" * (WINDOW - 1) + peptide + "-" * (WINDOW - 1))
num_peaks = len(peptide) + WINDOW - 1
for i in xrange(0, num_peaks):
kmer = flanked_peptide[i:i + WINDOW]
if "-" not in kmer:
for aa in kmer:
errors[aa].append(discr_signal[i] - model_signal[i])
return errors
def flip(blockades, model_file):
"""
Flips blockades
"""
blockade_model = load_model(model_file)
identifier = Identifier(blockade_model)
peptide = blockades[0].peptide
clusters = sp.preprocess_blockades(blockades, cluster_size=1,
min_dwell=0.0, max_dwell=1000)
print("Num\tFwd_dst\tRev_dst\t\tNeeds_flip", file=sys.stderr)
num_reversed = 0
new_blockades = []
for num, cluster in enumerate(clusters):
discr_signal = sp.discretize(cluster.consensus, len(peptide))
fwd_dist = identifier.signal_protein_distance(discr_signal, peptide)
rev_dist = identifier.signal_protein_distance(discr_signal,
peptide[::-1])
print("{0}\t{1:5.2f}\t{2:5.2f}\t\t{3}"
.format(num + 1, fwd_dist, rev_dist, fwd_dist > rev_dist),
file=sys.stderr)
new_blockades.append(cluster.blockades[0])
if fwd_dist > rev_dist:
new_blockades[-1].eventTrace = new_blockades[-1].eventTrace[::-1]
num_reversed += 1
print("Reversed:", num_reversed, "of", len(blockades), file=sys.stderr)
return new_blockades
def get_bias(blockades_file, model_file, cluster_size):
"""
Gets AA-specific bias between the empirical and theoretical signals
"""
WINDOW = 4
blockades = read_mat(blockades_file)
clusters = sp.preprocess_blockades(blockades, cluster_size=cluster_size,
min_dwell=0.5, max_dwell=20)
peptide = clusters[0].blockades[0].peptide
blockade_model = load_model(model_file)
errors = defaultdict(list)
model_signal = blockade_model.peptide_signal(peptide)
for cluster in clusters:
discr_signal = sp.discretize(cluster.consensus, len(peptide))
flanked_peptide = ("-" * (WINDOW - 1) + peptide +
"-" * (WINDOW - 1))
num_peaks = len(peptide) + WINDOW - 1
for i in xrange(0, num_peaks):
kmer = flanked_peptide[i : i + WINDOW]
if "-" not in kmer:
for aa in kmer:
errors[aa].append(discr_signal[i] - model_signal[i])
return errors
def _get_peptides_signals(mat_files):
TRAIN_AVG = 1
peptides = []
signals = []
for mat in mat_files:
blockades = read_mat(mat)
clusters = sp.preprocess_blockades(blockades, cluster_size=TRAIN_AVG, min_dwell=0.5, max_dwell=20)
mat_peptide = clusters[0].blockades[0].peptide
peptides.extend([mat_peptide] * len(clusters))
for cluster in clusters:
signals.append(sp.discretize(cluster.consensus, len(mat_peptide)))
return peptides, signals
def _get_peptides_signals(mat_files):
TRAIN_AVG = 1
peptides = []
signals = []
for mat in mat_files:
blockades = read_mat(mat)
clusters = sp.preprocess_blockades(blockades,
cluster_size=TRAIN_AVG,
min_dwell=0.5,
max_dwell=20)
mat_peptide = clusters[0].blockades[0].peptide
peptides.extend([mat_peptide] * len(clusters))
for cluster in clusters:
signals.append(sp.discretize(cluster.consensus, len(mat_peptide)))
return peptides, signals
def rank_db_proteins(self, signal):
"""
Rank database proteins wrt to the similarity to a given signal
"""
assert self.database is not None
distances = {}
discretized = {}
for prot_id, prot_seq in self.database.items():
if len(prot_seq) not in discretized:
discretized[len(prot_seq)] = sp.discretize(signal, len(prot_seq))
distance = self.signal_protein_distance(discretized[len(prot_seq)],
prot_seq)
distances[prot_id] = distance
return sorted(distances.items(), key=lambda i: i[1])
def correlation(mat_file_1, mat_file_2):
"""
Draws the plot
"""
blockades_1 = read_mat(mat_file_1)
blockades_1 = sp._fractional_blockades(blockades_1)
blockades_1 = sp._filter_by_duration(blockades_1, 0.5, 20)
blockades_1 = map(
lambda b: sp.discretize(sp._trim_flank_noise(b.eventTrace), 20),
blockades_1)
blockades_2 = read_mat(mat_file_2)
blockades_2 = sp._fractional_blockades(blockades_2)
blockades_2 = sp._filter_by_duration(blockades_2, 0.5, 20)
blockades_2 = map(
lambda b: sp.discretize(sp._trim_flank_noise(b.eventTrace), 20),
blockades_2)
self_corr = []
cross_corr = []
for blockade in blockades_1:
block_self = []
for other in blockades_1:
block_self.append(1 - distance.correlation(blockade, other))
block_cross = []
for other in blockades_2:
block_cross.append(1 - distance.correlation(blockade, other))
self_corr.append(np.mean(block_self))
cross_corr.append(np.mean(block_cross))
mean_self = np.median(self_corr)
mean_cross = np.median(cross_corr)
matplotlib.rcParams.update({"font.size": 16})
fig = plt.subplot()
fig.spines["right"].set_visible(False)
fig.spines["top"].set_visible(False)
fig.get_xaxis().tick_bottom()
fig.get_yaxis().tick_left()
fig.set_xlim(-0.6, 0.6)
fig.set_ylim(-0.6, 0.6)
fig.set_xlabel("(H3 tail, H3 tail) correlation")
fig.set_ylabel("(H3 tail, CCL5) correlation")
for y in [-0.4, -0.2, 0, 0.2, 0.4]:
plt.plot((-0.6, 0.6), (y, y), "--", lw=0.5, color="black")
plt.plot((y, y), (-0.6, 0.6), "--", lw=0.5, color="black")
plt.plot((-0.6, 0.6), (mean_cross, mean_cross), "--", lw=1.5, color="red")
plt.plot((mean_self, mean_self), (-0.6, 0.6), "--", lw=1.5, color="red")
fig.scatter(self_corr,
cross_corr,
linewidth=0.5,
c="dodgerblue",
s=30,
edgecolor="blue")
plt.tight_layout()
plt.show()