def plot_Δcq(df):
def iter_keys():
for sgrna in ['on', 'off', '11', '30']:
for time in [30, 60, 90, 120]:
for ligand in [False, True]:
yield sgrna, time, ligand
def name_from_key(key):
sgrna, time, ligand = key
return f"{sgrna} {time} ({'holo' if ligand else 'apo'})"
fig, ax = plt.subplots(1, figsize=(6, 4))
xticks = []
xtick_labels = []
for i, key in enumerate(iter_keys()):
row = df.loc[key]
sgrna, time, ligand = key
sgrna = {
'on': 'on',
'off': 'off',
'11': 'rxb/11/1',
'30': 'mhf/30'
}[sgrna]
fold_change = 2**(-row['Δcq_mean'])
fold_change_bound = 2**(-row['Δcq_mean'] + row['Δcq_std'])
fold_change_err = fold_change_bound - fold_change
x = [i, i]
y = [0, fold_change]
style = dict(
linewidth=5,
color=pick_color(sgrna),
)
ax.plot(x, y, **style)
x = x[-1:]
y = y[-1:]
y_err = [fold_change_err]
style = dict(
ecolor=pick_color(sgrna),
capsize=2.5,
)
ax.errorbar(x, y, y_err, **style)
xticks.append(i)
xtick_labels.append(name_from_key(key))
ax.set_ylabel('GFP Expression\n[rel. to 16S rRNA]')
ax.set_xlim(xticks[0] - 0.5, xticks[-1] + 0.5)
ax.set_xticks(xticks)
ax.set_xticklabels(xtick_labels, rotation='vertical')
ax.grid(axis='y')
#ax.yaxis.set_major_locator(FoldChangeLocator())
fig.tight_layout(pad=0)
return fig
def plot_Δct_data(df):
def iter_keys():
for promoter in ['j23119', 'j23150']:
for sgrna in ['on', 'off', 'rxb 11,1']:
for ligand in [False, True]:
yield promoter, sgrna, ligand
def name_from_key(key):
promoter, sgrna, ligand = key
return f"{promoter} {sgrna} ({'holo' if ligand else 'apo'})"
fig, ax = plt.subplots(1, figsize=(4, 3.25))
xticks = []
xtick_labels = []
for i, key in enumerate(iter_keys()):
row = df.loc[key]
promoter, sgrna, ligand = key
fold_change = 2**(-row['Δct_mean'])
fold_change_bound = 2**(-row['Δct_mean'] + row['Δct_std'])
fold_change_err = fold_change_bound - fold_change
x = [i, i]
y = [0, fold_change]
style = dict(
linewidth=5,
color=pick_color(sgrna),
)
ax.plot(x, y, **style)
x = x[-1:]
y = y[-1:]
y_err = [fold_change_err]
style = dict(
ecolor=pick_color(sgrna),
capsize=2.5,
)
ax.errorbar(x, y, y_err, **style)
xticks.append(i)
xtick_labels.append(name_from_key(key))
ax.set_ylabel('RNA Expression\n[rel. to 16S rRNA]')
ax.set_xlim(xticks[0] - 0.5, xticks[-1] + 0.5)
ax.set_xticks(xticks)
ax.set_xticklabels(xtick_labels, rotation='vertical')
ax.grid(axis='y')
#ax.yaxis.set_major_locator(FoldChangeLocator())
fig.tight_layout(pad=0)
#fig.savefig('20181002_sgrna_qpcr_Δct.svg')
return fig
def plot_ΔΔct_data(df):
keys = [
('on', False),
('on', True ),
('off', False),
('off', True ),
('rxb 11,1', False),
('rxb 11,1', True ),
]
def name_from_key(key):
sgrna, ligand = key
return f"{sgrna} ({'holo' if ligand else 'apo'})"
fig, ax = plt.subplots(1, figsize=(4, 3))
xticks = []
xtick_labels = []
for i, key in enumerate(keys):
row = df.loc[key]
sgrna, ligand = key
x = [i, i]
y = [0, row['fold_change']]
style = dict(
linewidth=5,
color=pick_color(sgrna),
)
ax.plot(x, y, **style)
x = x[-1:]
y = y[-1:]
y_err = [row['upper_err']]
style = dict(
ecolor=pick_color(sgrna),
capsize=2.5,
)
ax.errorbar(x, y, y_err, **style)
xticks.append(i)
xtick_labels.append(name_from_key(key))
ax.set_ylabel('J23119 / J23150')
ax.set_xlim(xticks[0] - 0.5, xticks[-1] + 0.5)
ax.set_xticks(xticks)
ax.set_xticklabels(xtick_labels, rotation='vertical')
ax.grid(axis='y')
ax.yaxis.set_major_locator(FoldChangeLocator())
fig.tight_layout(pad=0)
fig.savefig('20181002_sgrna_qpcr_ΔΔct.svg')
return fig
def plot_miller_units(rxns, stem, figure_mode=False):
if figure_mode:
return
num_spacers = len({rxn.spacer for i, rxn in iter_reactions(rxns)})
fig, axes = plt.subplots(4, 1, figsize=(1 + 3 * num_spacers, 8))
style = dict(linewidth=5)
xticks = []
for i, rxn in iter_reactions(rxns):
style = dict(
linewidth=5,
color=pick_color(rxn.sgrna),
)
raw_style = dict(
linewidth=5,
color=pick_color(rxn.sgrna, lightness=2),
)
x = [i, i]
ys = [
(0, [0, rxn.fit[0]], style),
(1, [0, rxn.raw_od600], raw_style),
(1, [0, rxn.od600], style),
(2, [0, rxn.vol_mL], style),
(3, [0, rxn.miller], style),
]
xticks.append((i, f'{rxn.apo_holo} {rxn.label}'))
for j, y, style in ys:
axes[j].plot(x, y, **style)
xticks, xticklabels = zip(*xticks)
axes[3].set_xticklabels(xticklabels, rotation='vertical')
for ax in axes:
ax.set_ylim(0, ax.get_ylim()[1])
ax.set_xlim(xticks[0] - 0.5, xticks[-1] + 0.5)
ax.set_xticks(xticks)
if ax is not axes[3]:
ax.tick_params(labelbottom=False)
axes[0].set_ylabel('d[A420]/dt')
axes[1].set_ylabel('OD600')
axes[2].set_ylabel('volume (mL)')
axes[3].set_ylabel('Miller units')
finalize_plot(fig, stem, 'miller')
def _plot_condition(self, x, experiment, condition, wells):
# Plot the fluorescence data.
ys = np.array([x.linear_loc for x in wells])
xs = np.full(ys.shape, x)
y = np.mean(ys)
err = np.std(ys)
label = experiment.get('family', experiment['label']).lower()
bar_color = pick_color(label)
dot_colors = pick_dot_colors(label, ys)
self.axes.plot(
[x, x],
[0, y],
color=bar_color,
linewidth=self.bar_width,
solid_capstyle='butt',
zorder=1,
)
self.axes.scatter(
xs,
ys,
c=dot_colors,
marker='+',
zorder=2,
)
#self.axes.errorbar(
# x, y, yerr=err,
# ecolor=color,
# capsize=self.bar_width / 2,
#)
# Label the axis.
self.x_ticks.append(x)
self.x_tick_labels.append(condition)
def plot_fold_changes(rxns, stem, figure_mode=False):
primary_keys, secondary_keys = load_keys(rxns)
n1, n2 = len(primary_keys), len(secondary_keys)
# 1.739: To get same axes height at TMP MIC panel.
wh = (3.325, 1.739) if figure_mode else (1 + 1* len(primary_keys), 3)
fig, ax = plt.subplots(figsize=wh)
bar_style = dict(zorder=1, linewidth=5)
dot_style = dict(zorder=2, marker='+')
xticks = []
apos = {}
holos = {}
miller_units = {False: [], True: []}
fold_changes = {}
sgrnas = {}
replicates = {}
for i, rxn in iter_reactions(rxns):
sgrnas[rxn.key] = rxn.sgrna
replicates.setdefault(rxn.key, {})\
.setdefault(rxn.ligand, [])\
.append(rxn)
for key in replicates:
sgrna = sgrnas[key]
apos = np.array([x.miller for x in replicates[key][False]])
holos = np.array([x.miller for x in replicates[key][True]])
fold_changes = holos / apos
#i = primary_keys[key[0]]
#j = secondary_keys[key[1]]
#x0 = i * (len(primary_keys) + 1) + j
#n = len(fold_changes)
#for k, fold_change in enumerate(fold_changes):
# x = [x0*n + k, x0*n + k]
# y = [0, fold_change if fold_change > 1 else 1/fold_change]
# xticks.append((x[0], ' '.join(key)))
# ax.plot(x, y, color=colors[key], **style)
i = primary_keys[key[0]]
j = secondary_keys[key[1]]
x = i * (len(primary_keys) + 1) + j
xticks.append((x, ' '.join(key)))
# For visualization purposes, I always want the fold_change to be
# greater than one. But I don't want to enforce this until after the
# mean fold change has been calculated.
if np.mean(fold_changes) < 1:
fold_changes = 1 / fold_changes
ys = fold_changes
xs = np.full(ys.shape, x)
y = np.mean(ys)
yerr = np.std(ys)
ax.plot([x,x], [0,y], color=pick_color(sgrna), **bar_style)
ax.scatter(xs, ys, color=pick_dot_colors(sgrna, ys), **dot_style)
#ax.errorbar([x], [y], yerr, color=colors[key])
xticks, xticklabels = zip(*xticks)
ax.set_xlim(xticks[0] - 0.5, xticks[-1] + 0.5)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation='vertical')
ax.set_ylabel('Fold change')
ax.set_ylim(0, ax.get_ylim()[1])
ax.yaxis.set_major_locator(FoldChangeLocator(6))
ax.yaxis.grid()
finalize_plot(fig, stem, 'fold')
def plot_miller_units(rxns, stem, figure_mode=False):
primary_keys, secondary_keys = load_keys(rxns)
n1, n2 = len(primary_keys), len(secondary_keys)
fig, ax = plt.subplots(figsize=(1 + 1 * len(primary_keys), 3))
bar_style = dict(zorder=1, linewidth=5)
dot_style = dict(zorder=2, marker='+')
xticks = []
apos = {}
holos = {}
miller_units = {False: [], True: []}
fold_changes = {}
sgrnas = {}
replicates = {}
for i, rxn in iter_reactions(rxns):
sgrnas[rxn.key] = rxn.sgrna
replicates.setdefault(rxn.key, {})\
.setdefault(rxn.ligand, [])\
.append(rxn)
for key in replicates:
apos = np.array([x.miller for x in replicates[key][False]])
holos = np.array([x.miller for x in replicates[key][True]])
i = primary_keys[key[0]]
j = secondary_keys[key[1]]
x = i * (len(primary_keys) + 1) + j
x_apo = 2 * x
x_holo = 2 * x + 1
xs_apo = np.full(apos.shape, x_apo)
xs_holo = np.full(apos.shape, x_holo)
xticks.append((x_apo, ' '.join(key + ('apo',))))
xticks.append((x_holo, ' '.join(key + ('holo',))))
y_apo = np.mean(apos)
y_apo_err = np.std(apos)
y_holo = np.mean(holos)
y_holo_err = np.std(holos)
bar_color = pick_color(sgrnas[key])
apo_dot_colors = pick_dot_colors(sgrnas[key], apos)
holo_dot_colors = pick_dot_colors(sgrnas[key], holos)
ax.plot([x_apo,x_apo], [0,y_apo], color=bar_color, **bar_style)
ax.plot([x_holo,x_holo], [0,y_holo], color=bar_color, **bar_style)
ax.scatter(xs_apo, apos, c=apo_dot_colors, **dot_style)
ax.scatter(xs_holo, holos, c=holo_dot_colors, **dot_style)
#ax.errorbar([x_apo], [y_apo], y_apo_err, color=colors[key])
#ax.errorbar([x_holo], [y_holo], y_holo_err, color=colors[key])
xticks, xticklabels = zip(*xticks)
ax.set_xlim(xticks[0] - 0.5, xticks[-1] + 0.5)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation='vertical')
ax.set_ylabel('Miller Units')
ax.set_ylim(0, ax.get_ylim()[1])
ax.yaxis.set_major_locator(FoldChangeLocator(6))
ax.yaxis.grid()
fig.tight_layout()
finalize_plot(fig, stem, 'miller')