def vdom_pssm(pssm, letter_width=0.2, letter_height=0.8, **kwargs):
"""Nicely plot the pssm
"""
import matplotlib.pyplot as plt
from concise.utils.plot import seqlogo_fig, seqlogo
fig, ax = plt.subplots(figsize=(letter_width * len(pssm), letter_height))
ax.axison = False
seqlogo(pssm, ax=ax)
plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
return fig2vdom(fig, **kwargs)
def seqlogo_clean(seq, letter_width=0.2, height=0.8, title=None):
import matplotlib.pyplot as plt
from concise.utils.plot import seqlogo
fig, ax = plt.subplots(figsize=(letter_width * len(seq), height))
ax.axison = False
seqlogo(seq, ax=ax)
if title is not None:
ax.set_title(title)
else:
plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
return fig
def plot_filters(W, ncol=2, figsize=(10,10)):
"""Plot convolutional filters as motifs
Args:
weights: weights returned by `model.layers[0].get_weights()[0]`
ncol: number of columns in the plot
figsize: Matplotlib figure size (width, height)
"""
N = W.shape[2]
nrow = int(np.ceil(N/ncol))
fig, ax = plt.subplots(nrow, ncol, figsize=figsize)
for i in range(N):
ax = fig.axes[i]
seqlogo(W[:,:,i], ax=ax);
ax.set_title(f"Filter: {i}")
plt.tight_layout()
def plot_profiles(self,
x,
tracks,
contribution_scores={},
figsize=(20, 2),
rc_vec=None,
start_vec=None,
width=20,
legend=True,
seq_height=2,
ylim=[0, 3],
n_limit=35,
n_bootstrap=None,
pattern_names=None,
fpath_template=None,
rc_fn=lambda x: x[::-1, ::-1]):
"""
Plot the sequence profiles
Args:
x: one-hot-encoded sequence
tracks: dictionary of profile tracks
contribution_scores: optional dictionary of contribution scores
TODO - add the reverse complementation option to it
"""
import matplotlib.pyplot as plt
from concise.utils.plot import seqlogo_fig, seqlogo
seqs_all = self.extract_signal(x)
ext_contribution_scores = {
s: self.extract_signal(contrib)
for s, contrib in contribution_scores.items()
}
# TODO assert correct shape in contrib
if pattern_names is None:
pattern_names = self.pattern_names()
for i, pattern in enumerate(pattern_names):
j = i
seqs = seqs_all[pattern]
sequence = ic_scale(seqs.mean(axis=0))
if rc_vec is not None and rc_vec[i]:
rc_seq = True
sequence = rc_fn(sequence)
else:
rc_seq = False
if start_vec is not None:
start = start_vec[i]
sequence = sequence[start:(start + width)]
n = len(seqs)
if n < n_limit:
continue
fig, ax = plt.subplots(1 + len(contribution_scores) + len(tracks),
1,
sharex=True,
figsize=figsize,
gridspec_kw={
'height_ratios':
[1] * len(tracks) + [seq_height] *
(1 + len(contribution_scores))
})
ax[0].set_title(f"{pattern} ({n})")
for i, (k, y) in enumerate(tracks.items()):
signal = self.extract_signal(y, rc_fn)[pattern]
if start_vec is not None:
start = start_vec[i]
signal = signal[:, start:(start + width)]
if n_bootstrap is None:
signal_mean = signal.mean(axis=0)
signal_std = signal.std(axis=0)
else:
signal_mean, signal_std = bootstrap_mean(signal,
n=n_bootstrap)
if rc_vec is not None and rc_vec[i]:
signal_mean = rc_fn(signal_mean)
signal_std = rc_fn(signal_std)
ax[i].plot(np.arange(1,
len(signal_mean) + 1),
signal_mean[:, 0],
label='pos')
if n_bootstrap is not None:
ax[i].fill_between(
np.arange(1,
len(signal_mean) + 1),
signal_mean[:, 0] - 2 * signal_std[:, 0],
signal_mean[:, 0] + 2 * signal_std[:, 0],
alpha=0.1)
# label='pos')
# plot also the other strand
if signal_mean.shape[1] == 2:
ax[i].plot(np.arange(1,
len(signal_mean) + 1),
signal_mean[:, 1],
label='neg')
if n_bootstrap is not None:
ax[i].fill_between(
np.arange(1,
len(signal_mean) + 1),
signal_mean[:, 1] - 2 * signal_std[:, 1],
signal_mean[:, 1] + 2 * signal_std[:, 1],
alpha=0.1)
# label='pos')
ax[i].set_ylabel(f"{k}")
ax[i].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax[i].spines['top'].set_visible(False)
ax[i].spines['right'].set_visible(False)
ax[i].spines['bottom'].set_visible(False)
ax[i].xaxis.set_ticks_position('none')
if isinstance(ylim[0], list):
ax[i].set_ylim(ylim[i])
if legend:
ax[i].legend()
for k, (contrib_score_name,
values) in enumerate(ext_contribution_scores.items()):
ax_id = len(tracks) + k
logo = values[pattern].mean(axis=0)
# Trim the pattern if necessary
if rc_seq:
logo = rc_fn(logo)
if start_vec is not None:
start = start_vec[j]
logo = logo[start:(start + width)]
seqlogo(logo, ax=ax[ax_id])
ax[ax_id].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax[ax_id].set_ylabel(contrib_score_name)
ax[ax_id].spines['top'].set_visible(False)
ax[ax_id].spines['right'].set_visible(False)
ax[ax_id].spines['bottom'].set_visible(False)
ax[ax_id].xaxis.set_ticks_position('none')
seqlogo(sequence, ax=ax[-1])
ax[-1].yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
ax[-1].set_ylabel("Inf. content")
ax[-1].spines['top'].set_visible(False)
ax[-1].spines['right'].set_visible(False)
ax[-1].spines['bottom'].set_visible(False)
ax[-1].set_xticks(list(range(0, len(sequence) + 1, 5)))
if fpath_template is not None:
pname = pattern.replace("/", ".")
plt.savefig(fpath_template.format(pname) + '.png', dpi=600)
plt.savefig(fpath_template.format(pname) + '.pdf', dpi=600)
plt.close(fig) # close the figure
show_figure(fig)
plt.show()
def plot_profiles_single(seqlet,
x,
tracks,
importance_scores={},
figsize=(20, 2),
legend=True,
rotate_y=90,
seq_height=1,
flip_neg=False,
rc_fn=lambda x: x[::-1, ::-1]):
"""
Plot the sequence profiles
Args:
x: one-hot-encoded sequence
tracks: dictionary of profile tracks
importance_scores: optional dictionary of importance scores
"""
import matplotlib.pyplot as plt
from concise.utils.plot import seqlogo_fig, seqlogo
# --------------
# extract signal
seq = seqlet.extract(x)
ext_importance_scores = {
s: seqlet.extract(imp)
for s, imp in importance_scores.items()
}
fig, ax = plt.subplots(1 + len(importance_scores) + len(tracks),
1,
sharex=True,
figsize=figsize,
gridspec_kw={
'height_ratios': [1] * len(tracks) +
[seq_height] * (1 + len(importance_scores))
})
# signal
for i, (k, signal) in enumerate(tracks.items()):
plot_stranded_profile(seqlet.extract(signal),
ax=ax[i],
flip_neg=flip_neg)
simple_yaxis_format(ax[i])
strip_axis(ax[i])
ax[i].set_ylabel(f"{k}", rotation=rotate_y, ha='right', labelpad=5)
if legend:
ax[i].legend()
# -----------
# importance scores (seqlogo)
# -----------
max_scale = max([
np.maximum(v, 0).sum(axis=-1).max()
for v in ext_importance_scores.values()
])
min_scale = min([
np.minimum(v, 0).sum(axis=-1).min()
for v in ext_importance_scores.values()
])
for k, (imp_score_name, logo) in enumerate(ext_importance_scores.items()):
ax_id = len(tracks) + k
# plot
ax[ax_id].set_ylim([min_scale, max_scale])
ax[ax_id].axhline(y=0, linewidth=1, linestyle='--', color='grey')
seqlogo(logo, ax=ax[ax_id])
# style
simple_yaxis_format(ax[ax_id])
strip_axis(ax[ax_id])
# ax[ax_id].set_ylabel(imp_score_name)
ax[ax_id].set_ylabel(imp_score_name,
rotation=rotate_y,
ha='right',
labelpad=5) # va='bottom',
# -----------
# information content (seqlogo)
# -----------
# plot
seqlogo(seq, ax=ax[-1])
# style
simple_yaxis_format(ax[-1])
strip_axis(ax[-1])
ax[-1].set_ylabel("Inf. content",
rotation=rotate_y,
ha='right',
labelpad=5)
ax[-1].set_xticks(list(range(0, len(seq) + 1, 5)))
return fig
def plot_profiles(
seqlets_by_pattern,
x,
tracks,
importance_scores={},
figsize=(20, 2),
start_vec=None,
width=20,
legend=True,
rotate_y=90,
seq_height=1,
ymax=None, # determine y-max
n_limit=35,
n_bootstrap=None,
flip_neg=False,
patterns=None,
fpath_template=None,
only_idx=None,
mkdir=False,
rc_fn=lambda x: x[::-1, ::-1]):
"""
Plot the sequence profiles
Args:
x: one-hot-encoded sequence
tracks: dictionary of profile tracks
importance_scores: optional dictionary of importance scores
"""
import matplotlib.pyplot as plt
from concise.utils.plot import seqlogo_fig, seqlogo
# Setup start-vec
if start_vec is not None:
if not isinstance(start_vec, list):
start_vec = [start_vec] * len(patterns)
else:
start_vec = [0] * len(patterns)
width = len(x)
if patterns is None:
patterns = list(seqlets_by_pattern)
# aggregated profiles
d_signal_patterns = {
pattern: {
k: aggregate_profiles(extract_signal(
y, seqlets_by_pattern[pattern])[:,
start_vec[ip]:(start_vec[ip] +
width)],
n_bootstrap=n_bootstrap,
only_idx=only_idx)
for k, y in tracks.items()
}
for ip, pattern in enumerate(patterns)
}
if ymax is None:
# infer ymax
def take_max(x, dx):
if dx is None:
return x.max()
else:
# HACK - hard-coded 2
return (x + 2 * dx).max()
ymax = [
max([
take_max(*d_signal_patterns[pattern][k])
for pattern in patterns
]) for k in tracks
] # loop through all the tracks
if not isinstance(ymax, list):
ymax = [ymax] * len(tracks)
figs = []
for i, pattern in enumerate(tqdm(patterns)):
j = i
# --------------
# extract signal
seqs = extract_signal(
x,
seqlets_by_pattern[pattern])[:,
start_vec[i]:(start_vec[i] + width)]
ext_importance_scores = {
s: extract_signal(
imp, seqlets_by_pattern[pattern])[:,
start_vec[i]:(start_vec[i] +
width)]
for s, imp in importance_scores.items()
}
d_signal = d_signal_patterns[pattern]
# --------------
if only_idx is None:
sequence = ic_scale(seqs.mean(axis=0))
else:
sequence = seqs[only_idx]
n = len(seqs)
if n < n_limit:
continue
fig, ax = plt.subplots(1 + len(importance_scores) + len(tracks),
1,
sharex=True,
figsize=figsize,
gridspec_kw={
'height_ratios': [1] * len(tracks) +
[seq_height] * (1 + len(importance_scores))
})
# signal
ax[0].set_title(f"{pattern} ({n})")
for i, (k, signal) in enumerate(d_signal.items()):
signal_mean, signal_std = d_signal_patterns[pattern][k]
plot_stranded_profile(signal_mean,
ax=ax[i],
ymax=ymax[i],
profile_std=signal_std,
flip_neg=flip_neg)
simple_yaxis_format(ax[i])
strip_axis(ax[i])
ax[i].set_ylabel(f"{k}", rotation=rotate_y, ha='right', labelpad=5)
if legend:
ax[i].legend()
# -----------
# importance scores (seqlogo)
# -----------
# average the importance scores
if only_idx is None:
norm_importance_scores = {
k: v.mean(axis=0)
for k, v in ext_importance_scores.items()
}
else:
norm_importance_scores = {
k: v[only_idx]
for k, v in ext_importance_scores.items()
}
max_scale = max([
np.maximum(v, 0).sum(axis=-1).max()
for v in norm_importance_scores.values()
])
min_scale = min([
np.minimum(v, 0).sum(axis=-1).min()
for v in norm_importance_scores.values()
])
for k, (imp_score_name,
logo) in enumerate(norm_importance_scores.items()):
ax_id = len(tracks) + k
# Trim the pattern if necessary
# plot
ax[ax_id].set_ylim([min_scale, max_scale])
ax[ax_id].axhline(y=0, linewidth=1, linestyle='--', color='grey')
seqlogo(logo, ax=ax[ax_id])
# style
simple_yaxis_format(ax[ax_id])
strip_axis(ax[ax_id])
# ax[ax_id].set_ylabel(imp_score_name)
ax[ax_id].set_ylabel(imp_score_name,
rotation=rotate_y,
ha='right',
labelpad=5) # va='bottom',
# -----------
# information content (seqlogo)
# -----------
# plot
seqlogo(sequence, ax=ax[-1])
# style
simple_yaxis_format(ax[-1])
strip_axis(ax[-1])
ax[-1].set_ylabel("Inf. content",
rotation=rotate_y,
ha='right',
labelpad=5)
ax[-1].set_xticks(list(range(0, len(sequence) + 1, 5)))
figs.append(fig)
# save to file
if fpath_template is not None:
pname = pattern.replace("/", ".")
basepath = fpath_template.format(pname=pname, pattern=pattern)
if mkdir:
os.makedirs(os.path.dirname(basepath), exist_ok=True)
plt.savefig(basepath + '.png', dpi=600)
plt.savefig(basepath + '.pdf', dpi=600)
plt.close(fig) # close the figure
show_figure(fig)
plt.show()
return figs
def plot_patterns(patterns,
tasks,
pattern_trim=(24, 41),
fp_slice=slice(10, 190),
n_blank=1):
df_info = get_df_info(patterns, tasks)
max_vals = {t: df_info.max()[t + "_max"] for t in tasks}
fig, axes = plt.subplots(len(patterns),
3 + len(tasks),
figsize=get_figsize(1, aspect=1.2))
fig.subplots_adjust(hspace=0, wspace=0)
# Get the ylim for each TF
contrib_ylim = {
tf: (min([
p.contrib[p.attrs['TF']].min() for p in patterns
if p.attrs['TF'] == tf
]),
max([
p.contrib[p.attrs['TF']].max() for p in patterns
if p.attrs['TF'] == tf
]))
for tf in tasks
}
max_digits = max([len(str(p.attrs["n_seqlets"])) for p in patterns])
for i, p in enumerate(patterns):
# Motif logo
ax = axes[i, 0]
p = p.trim(*pattern_trim).rc()
seqlogo(p.contrib[p.attrs['TF']], ax=ax)
ax.set_ylim(
contrib_ylim[p.attrs['TF']]) # all plots have the same shape
strip_axis(ax)
ax.axison = False
pos1 = ax.get_position() # get the original position
extra_x = pos1.width * 0.2
pos2 = [
pos1.x0, pos1.y0 + pos1.height * 0.4, pos1.width + extra_x,
pos1.height * .5
]
ax.set_position(pos2) # set a new position
if i == 0:
ax.set_title("Importance\nscore")
# Text columns before
if "/" in p.name:
pid = p.name.split("_")[-1]
else:
pid = p.name.replace("m0_p", "")
# Oct4/1 150
# str_n_seqlets = "%*d" % (max_digits, p.attrs["n_seqlets"])
ax.text(-9,
0,
p.attrs["TF"] + "/" + pid,
fontsize=8,
horizontalalignment='right')
ax.text(-1,
0,
str(p.attrs['n_seqlets']),
fontsize=8,
horizontalalignment='right')
ax = axes[i, 1]
seqlogo(p.get_seq_ic(), ax=ax)
ax.set_ylim([0, 2]) # all plots have the same shape
strip_axis(ax)
ax.axison = False
pos1 = ax.get_position() # get the original position
pos2 = [
pos1.x0 + extra_x, pos1.y0 + pos1.height * 0.4,
pos1.width + extra_x, pos1.height * .5
]
ax.set_position(pos2) # set a new position
if i == 0:
ax.set_title("Sequence\ninfo. content")
# ax.text(22, 1, i_to_motif_names[i], fontsize=8, horizontalalignment='center')
ax = axes[i, 2]
blank_ax(ax)
pos1 = ax.get_position() # get the original position
pos2 = [
pos1.x0 + extra_x * 2, pos1.y0 + pos1.height * 0.4,
pos1.width - 2 * extra_x, pos1.height * .5
]
ax.set_position(pos2) # set a new position
# if i == 0:
# ax.set_title("Assumed\nmotif")
# Profile columns
for j, task in enumerate(tasks):
ax = axes[i, 3 + j]
fp = p.profile[task]
ax.plot(fp[fp_slice, 0], color=tf_colors[task])
ax.plot(-fp[fp_slice, 1], color=tf_colors[task],
alpha=0.5) # negative strand
ax.set_ylim([-max_vals[task],
max_vals[task]]) # all plots have the same shape
strip_axis(ax)
ax.axison = False
if i == 0:
ax.set_title(task)
return fig
print("unsuccessfull parsing. letter: " + let)
# A, D, O, P
dragonn.plot.standardize_polygons_str(O)
ax = plt.gca()
add_letter_to_axis(ax, "P", 0, 0, 1)
plt.show()
all_polygons = {l: globals()[l] for l in string.ascii_uppercase}
seqlogo(pwm, "DNA")
plt.show()
seqlogo(pwm, "RNA")
plt.show()
seqlogo(pwm, "AA")
plt.show()
aa_matrix = np.random.uniform(0, 1, (10, len(VOCAB_colors["AA"])))
seqlogo(aa_matrix, "AA")
plt.show()
aa_matrix = np.random.uniform(-1, 1, (10, len(VOCAB_colors["AA"])))
seqlogo(aa_matrix, "AA")