def akshay_partial_summarize(name_pattern, group_by, av_nuc_p53, av_en_p53,
output):
with open(output, 'ab') as output_file:
writer = csv_writer(output_file)
for i, nuc_pac in enumerate(zip(av_nuc_p53, av_en_p53)):
writer.writerow(
[name_pattern, group_by, i, nuc_pac[0], nuc_pac[1]])
def linhao_secondary_summarize(primary_namespace, output):
with open(output, 'ab') as output_file:
writer = csv_writer(output_file)
namespace = primary_namespace['name pattern']
tag_group = primary_namespace['group id']
total_cells = len(np.unique(primary_namespace['pre_cell_labels'])) - 1
analyzed_cells = len(np.unique(primary_namespace['cell_labels'])) - 1
writer.writerow([namespace] + tag_group + [total_cells, analyzed_cells])
return primary_namespace
def Kristen_summarize_a(name_pattern, q_mean, q_median, q_std, nq_mean,
nq_median, nq_std, slope, r2, p, output):
with open(output, 'ab') as output_file:
# csv_read = csv.res
writer = csv_writer(output_file, delimiter='\t')
for i in name_pattern:
writer.writerow([
i, q_mean, q_median, q_std, nq_mean, nq_median, nq_std, slope,
r2, p
])
def linhao_summarize(primary_namespace, output):
with open(output, 'ab') as output_file:
writer = csv_writer(output_file)
namespace = primary_namespace['name pattern']
tag_group = primary_namespace['group id']
secondary_namespace = primary_namespace['per_cell']
pre_puck = [namespace] + tag_group
for key, value in secondary_namespace.iteritems():
if key != '_pad':
proper_puck = pre_puck+[key, value['gfp_mqvi'], value['mch_mqvi'], value['final_classification']]
writer.writerow(proper_puck)
return primary_namespace
def xi_pre_render(name_pattern,
proj_gfp,
qual_gfp,
cell_labels,
average_gfp_pad,
proj_mch,
mch,
gfp,
timestamp,
save=False,
directory_to_save_to='verification',
mch_cutoff=0.2,
slector_cutoff=0.1):
plt.figure(figsize=(20, 15))
plt.suptitle(name_pattern)
main_ax = plt.subplot(231)
plt.title('GFP')
plt.imshow(proj_gfp, interpolation='nearest')
plt.contour(cell_labels > 0, [0.5], colors='w')
plt.subplot(232, sharex=main_ax, sharey=main_ax)
plt.title('log-GFP')
plt.imshow(np.log(proj_gfp + np.min(proj_gfp[proj_gfp > 0])),
cmap='hot',
interpolation='nearest')
plt.contour(cell_labels > 0, [0.5], colors='w')
plt.subplot(233, sharex=main_ax, sharey=main_ax)
plt.title('raw segmentation')
plt.imshow(qual_gfp, cmap='gray', interpolation='nearest')
plt.contour(cell_labels > 0, [0.5], colors='w')
ax = plt.subplot(234, sharex=main_ax, sharey=main_ax)
plt.title('labeled segmentation')
plt.imshow(cell_labels, cmap=plt.cm.spectral, interpolation='nearest')
unique = np.unique(cell_labels)
for i in unique:
mask = cell_labels == i
x, y = scipy.ndimage.measurements.center_of_mass(mask)
ax.text(y - 8, x + 8, '%s' % i, fontsize=10)
plt.subplot(235)
selector = np.logical_and(mch > slector_cutoff, gfp > slector_cutoff)
plt.title('mCh-GFP correlation - %s, qual GFP intensity: %s' %
(np.corrcoef(mch[selector], gfp[selector])[0, 1],
np.median(gfp[mch > mch_cutoff])))
slope, intercept, rvalue, pvalue, stderr = linregress(
mch[selector], gfp[selector])
better2D_desisty_plot(mch[selector], gfp[selector])
linarray = np.arange(0.1, 0.5, 0.05)
plt.plot(linarray, intercept + slope * linarray, 'r')
plt.xlabel('mCherry')
plt.ylabel('GFP')
plt.subplot(236, sharex=main_ax, sharey=main_ax)
plt.title('mCherry')
plt.imshow(proj_mch, interpolation='nearest')
plt.contour(cell_labels > 0, [0.5], colors='w')
with open('xi_analys_results.csv', 'ab') as output_file:
writer = csv_writer(output_file)
puck = [
name_pattern, timestamp,
np.corrcoef(mch[selector], gfp[selector])[0, 1],
np.median(gfp[mch > mch_cutoff]),
np.average(gfp[mch > mch_cutoff]), slope, rvalue, pvalue
]
writer.writerow(puck)
if not save:
plt.show()
else:
name_puck = directory_to_save_to + '/' + 'xi_pre_render-' + timestamp + '-' + name_pattern + '.png'
plt.savefig(name_puck)
plt.close()
def Kristen_render(name_pattern,
group_id,
mCherry,
extranuclear_mCherry_pad,
GFP_orig,
mCherry_orig,
output,
save=False,
directory_to_save_to='verification'):
labels, _ = ndi.label(extranuclear_mCherry_pad)
unique_segmented_cells_labels = np.unique(labels)[1:]
mCherry_cutoff = np.zeros_like(mCherry)
qualifying_cell_label = []
qualifying_regression_stats = []
for cell_label in unique_segmented_cells_labels:
mCherry_2 = np.zeros_like(mCherry)
my_mask = labels == cell_label
average_apply_mask = np.mean(mCherry[my_mask])
intensity = np.sum(mCherry[my_mask])
binary_pad = np.zeros_like(mCherry)
binary_pad[my_mask] = 1
pixel = np.sum(binary_pad[my_mask])
if (average_apply_mask > .05 or intensity > 300) and pixel > 4000:
GFP_limited_to_cell_mask = imagepipe.raw_functions.f_3d_stack_2d_filter(
GFP_orig, my_mask)
mCherry_limited_to_cell_mask = imagepipe.raw_functions.f_3d_stack_2d_filter(
mCherry_orig, my_mask)
qualifying_3d_GFP = GFP_limited_to_cell_mask[
mCherry_limited_to_cell_mask > 50]
average_3d_GFP = np.mean(qualifying_3d_GFP)
median_3d_GFP = np.median(qualifying_3d_GFP)
std_3d_GFP = np.std(qualifying_3d_GFP)
sum_qualifying_GFP = np.sum(qualifying_3d_GFP)
nonqualifying_3d_GFP = GFP_limited_to_cell_mask[
mCherry_limited_to_cell_mask <= 50]
average_nonqualifying_3d_GFP = np.mean(nonqualifying_3d_GFP)
median_nonqualifying_3d_GFP = np.median(nonqualifying_3d_GFP)
std_nonqualifying_3d_GFP = np.std(nonqualifying_3d_GFP)
sum_nonqualifying_GFP = np.sum(nonqualifying_3d_GFP)
sum_total_GFP = sum_qualifying_GFP + sum_nonqualifying_GFP
percent_qualifying_over_total_GFP = sum_qualifying_GFP / sum_total_GFP
# report the percentage too or sums are sufficient?
GFP_orig_qualifying = imagepipe.raw_functions.f_3d_stack_2d_filter(
GFP_orig, my_mask)
mCherry_orig_qualifying = imagepipe.raw_functions.f_3d_stack_2d_filter(
mCherry_orig, my_mask)
mCherry_1d = mCherry_orig_qualifying[mCherry_orig_qualifying > 50]
GFP_1d = GFP_orig_qualifying[mCherry_orig_qualifying > 50]
regression_results = stats.linregress(GFP_1d, mCherry_1d)
mCherry_2[my_mask] = mCherry[my_mask]
mCherry_cutoff[my_mask] = mCherry[my_mask]
qualifying_cell_label.append(cell_label)
qualifying_regression_stats.append(
(regression_results[0], regression_results[2],
regression_results[3]))
name_pattern_split = name_pattern.split(' - ')
transfection_label = name_pattern_split[0]
cell_type = name_pattern_split[1]
exp_time = name_pattern_split[2]
image_number = name_pattern_split[4]
with open(output, 'ab') as output_file:
writer = csv_writer(output_file, delimiter='\t')
writer.writerow([
transfection_label, cell_type, exp_time, image_number,
cell_label, sum_qualifying_GFP, sum_total_GFP,
average_3d_GFP, median_3d_GFP, std_3d_GFP,
average_nonqualifying_3d_GFP, median_nonqualifying_3d_GFP,
std_nonqualifying_3d_GFP, regression_results[0],
regression_results[2], regression_results[3]
])
plt.figure(figsize=(26.0, 15.0))
plt.title('Kristen\'s Data')
plt.suptitle(name_pattern)
main_ax = plt.subplot(221)
plt.subplot(221, sharex=main_ax, sharey=main_ax)
plt.title('mCherry Binary')
im = plt.imshow(extranuclear_mCherry_pad,
interpolation='nearest',
cmap='hot')
plt.colorbar(im)
plt.subplot(222, sharex=main_ax, sharey=main_ax)
plt.title('mCherry')
plt.imshow(mCherry, interpolation='nearest')
plt.contour(extranuclear_mCherry_pad, [0.5], colors='k')
plt.subplot(223)
plt.better2D_desisty_plot(GFP_1d, mCherry_1d)
plt.title('mCherry Intensity as a Function of GFP Voxel')
plt.xlabel('GFP Voxel')
plt.ylabel('mCherry Intensity')
plt.subplot(224, sharex=main_ax, sharey=main_ax)
plt.title('mCherry-cutoff applied')
plt.imshow(mCherry_2, interpolation='nearest')
if not save:
plt.show()
else:
name_puck = directory_to_save_to + '/' + 'Kristen-' + name_pattern + '_cell' + str(
cell_label) + '.png'
plt.savefig(name_puck)
plt.close()
plt.figure(figsize=(26.0, 15.0))
main_ax = plt.subplot(121)
plt.subplot(121, sharex=main_ax, sharey=main_ax)
plt.suptitle('mCherry Before and After Qualifying Cell Cutoff is Applied')
plt.title('mCherry')
im = plt.imshow(mCherry, interpolation='nearest')
plt.colorbar(im)
plt.subplot(122, sharex=main_ax, sharey=main_ax)
plt.title('mCherry')
plt.imshow(mCherry_cutoff, interpolation='nearest')
if not save:
plt.show()
else:
name_puck = directory_to_save_to + '/' + 'Kristen-' + name_pattern + 'cutoff_app' + '.png'
plt.savefig(name_puck)
plt.close()
return qualifying_regression_stats