def plot_all_patterns(self,
kind='seq',
trim_frac=0,
n_min_seqlets=None,
ylim=None,
no_axis=False,
**kwargs):
"""
Args:
kind:
"""
self.stats() # print stats
for pattern in self.patterns():
if n_min_seqlets is not None and self.n_seqlets(
*pattern.split("/")) < n_min_seqlets:
continue
self.plot_pattern(pattern,
kind=kind,
trim_frac=trim_frac,
**kwargs)
if ylim is not None:
plt.ylim(ylim)
if no_axis:
strip_axis(plt.gca())
plt.gca().axison = False
def tidy_motif_plot(ax=None):
if ax is None:
ax = plt.gca()
strip_axis(ax)
ax.set_xlabel(None)
ax.get_xaxis().set_visible(False)
ax.set_ylim([0, 2.0])
ax.set_ylabel("IC")
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
def blank_ax(ax):
strip_axis(ax)
ax.axison = False