for rep in all_reps:
temp_hm, wt_temp = c_matrix(rep, aff_fun)
A_heatmaps.append(temp_hm)
A_wts.append(wt_temp)
A_heatmap = np.zeros(A_heatmaps[0].shape)
for ii in range(A_heatmap.shape[0]):
for jj in range(A_heatmap.shape[1]):
A_heatmap[ii, jj] = np.nanmedian(
[heatmap[ii, jj] for heatmap in A_heatmaps])
A_wt = np.median(A_wts)
# Affinity plot, lib1
ax = axes[0, 0]
labeler.label_subplot(ax, 'A')
A_1h_map = plot_panel(ax, A_heatmap[:10], A_wt, wtseq1, seq1pos, 'kd',
optseq1_dict)
ax.set_title('1H', fontsize=mpl.rcParams['font.size'])
# Affinity plot, lib2
ax = axes[0, 1]
labeler.label_subplot(ax, 'B')
A_3h_map = plot_panel(ax, A_heatmap[10:], A_wt, wtseq2, seq2pos, 'kd',
optseq2_dict)
ax.set_title('3H', fontsize=mpl.rcParams['font.size'])
# Get expression zero
E_heatmaps = []
E_wts = []
for rep in all_reps:
labelsize = 8
panelsize = 12
# Set colormaps
cmap = sns.cubehelix_palette(8, start=0.5, rot=0.0, reverse=True, as_cmap=True)
vmax = 100
vmin = 75
sns.set_style('white')
## RNAP heatmap
# Plot results for real RNAP data
ax = fig.add_axes([left, level1, hm_width, hm_height])
labeler.label_subplot(ax,'A',ypad_adjust=0.02)
sns.heatmap(
rnap_real_data.transpose(), annot=True, fmt="d", vmin=vmin, vmax=vmax,
annot_kws={"size": 7}, cmap=cmap, cbar_kws={"pad":.01})
gelx(ax,rnap_real_annotation,annotation_spacing=0.8,fontsize=labelsize)
gely(ax,rnap_real_ylabel,annotation_spacing=0.5,\
fontsize=labelsize,rotation=0,ha='right')
# Draw white lines
(num_cols,num_rows) = rnap_real_data.shape
for y in range(num_rows):
plt.plot([0,num_cols],[y,y],color='white',linewidth=2)
for x in range(0,num_cols,5):
plt.plot([x,x],[0,num_rows],color='white',linewidth=2)
## RNAP summary statistics
# Set colormaps
cmap = sns.cubehelix_palette(8, start=0.0, rot=0.0, reverse=True, as_cmap=True)
vmax = 100
vmin = 75
sns.set_style('white')
# Make a labler to add labels to subplots
labeler = Labeler(xpad=.07,ypad=0.02,fontsize=10)
## RNAP heatmap
# Plot results for real RNAP data
ax = fig.add_axes([left, level1, hm_width, hm_height])
labeler.label_subplot(ax,'A',xpad_adjust=0.03,ypad_adjust=0.04)
sns.heatmap(
df_rnap_comparison.transpose(), annot=True, fmt="d", vmin=vmin, vmax=vmax,
annot_kws={"size": 7}, cmap=cmap, cbar_kws={"pad":.03})
gelx(ax,df_rnap_xannotation,annotation_spacing=0.8,fontsize=labelsize)
gely(ax,df_rnap_yannotation,annotation_spacing=0.8,fontsize=labelsize,rotation=0)
# Draw white lines
(num_cols,num_rows) = df_rnap_comparison.shape
for y in range(num_rows):
plt.plot([0,num_cols],[y,y],color='white',linewidth=2)
for x in [1,6]:
plt.plot([x,x],[0,num_rows],color='white',linewidth=2)
## CRP heatmap
# Panel C
# Make a labler to add labels to subplots
labeler = Labeler(xpad=.07, ypad=-.01, fontsize=10)
# Position panel
#bottom=0.62
#top=0.98
bottom = 0.05
top = 0.30
left = 0.30
right = 0.75
height = top - bottom
width = right - left
ax = fig.add_axes([left, bottom, width, height])
labeler.label_subplot(ax, 'C', xpad_adjust=.05, ypad_adjust=0)
log_bounds = [-10, -4.5]
lims = log_bounds
R, P = plot_combine_clones(all_reps, log_bounds, ax)
ax.plot(lims, lims, '--', c=gray, zorder=-10)
ax.set_ylabel('$K_D$ [M], Tite-Seq', labelpad=2)
ax.set_xlabel('$K_D$ [M], flow', labelpad=2)
ticks = range(int(np.ceil(lims[0])), int(lims[1]) + 1)
tick_labels = [r'$10^{%i}$' % (t) for t in ticks]
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.set_xticklabels(tick_labels)
labelsize = 8
panelsize = 12
# Set colormaps
cmap = sns.cubehelix_palette(8, start=0.5, rot=0.0, reverse=True, as_cmap=True)
vmax = 100
vmin = 75
sns.set_style('white')
## RNAP heatmap
# Plot results for real RNAP data
ax = fig.add_axes([left, level1, hm_width, hm_height])
labeler.label_subplot(ax, 'A', ypad_adjust=0.02)
sns.heatmap(rnap_real_data.transpose(),
annot=True,
fmt="d",
vmin=vmin,
vmax=vmax,
annot_kws={"size": 7},
cmap=cmap,
cbar_kws={"pad": .01})
gelx(ax, rnap_real_annotation, annotation_spacing=0.8, fontsize=labelsize)
gely(ax,rnap_real_ylabel,annotation_spacing=0.5,\
fontsize=labelsize,rotation=0,ha='right')
# Draw white lines
(num_cols, num_rows) = rnap_real_data.shape
for y in range(num_rows):
right=.95,
hspace=0,
wspace=.5)
# Make a labler to add labels to subplots
labeler = Labeler(xpad=.03, ypad=-.01, fontsize=10)
# fluorescein grid
summary = get_clone_data()
clones = summary.keys()
inds = np.argsort(
[np.nanmean(np.log10(np.array(summary[k]['KD']))) for k in clones])
# Panel B
labeler.label_subplot(axes[0, 0], 'A')
fl = np.array([
0, 10**-9.5, 10**-9, 10**-8.5, 10**-8, 10**-7.5, 10**-7, 10**-6.5, 10**-6,
10**-5.5, 10**-5
])
plot_titeseq(axes[0, 0], all_reps, clones[inds[0]])
plot_titeseq(axes[1, 0], all_reps, clones[inds[1]])
plot_titeseq(axes[2, 0], all_reps, clones[inds[2]])
plot_titeseq(axes[3, 0], all_reps, clones[inds[3]])
plot_titeseq(axes[4, 0], all_reps, clones[inds[4]], ylabel=True)
plot_titeseq(axes[5, 0], all_reps, clones[inds[5]])
plot_titeseq(axes[6, 0], all_reps, clones[inds[6]])
plot_titeseq(axes[7, 0], all_reps, clones[inds[7]])
plot_titeseq(axes[8, 0], all_reps, clones[inds[8]])
plot_titeseq(axes[9, 0], all_reps, clones[inds[9]], xticklabels=True)
