floor_method='min',
alpha_clean=.5)
pipeline = wf.smooth_2d(pipeline, in_channel='DAPI',
smoothing_px=.5) # corresponds to a pixel of 1500 nm
print "2"
pipeline = wf.gamma_stabilize(pipeline,
in_channel='p53',
floor_method='min',
alpha_clean=.0)
pipeline = wf.smooth_2d(pipeline, in_channel='p53', smoothing_px=.5)
print "3"
pipeline = wf.robust_binarize(pipeline,
in_channel='DAPI',
out_channel=['nuclei'],
_dilation=0,
heterogeity_size=5,
feature_size=50)
pipeline = wf.label_and_correct(pipeline,
in_channel=['nuclei', 'DAPI'],
out_channel='nuclei',
min_px_radius=15,
min_intensity=20)
print "4"
# p53 segmentation
pipeline = wf.label_based_aq(pipeline,
in_channel=['nuclei', 'p53'],
out_channel=['av_p53', 'av_p53_pad'])
med_norm_mCh = wf.locally_normalize(med_norm_GFP,
in_channel='mCherry',
)
projected_GFP = wf.max_projection(med_norm_mCh,
in_channel='GFP',
out_channel='projected_GFP')
projected_mCh = wf.max_projection(projected_GFP,
in_channel='mCherry',
out_channel='projected_mCh')
binarized_GFP = wf.robust_binarize(projected_mCh,
in_channel='projected_GFP',
out_channel='cell_tags',
heterogeity_size=5,
feature_size=70,
)
segmented_GFP = wf.improved_watershed(binarized_GFP,
in_channel=['cell_tags', 'projected_mCh'],
out_channel='pre_cell_labels',
expected_separation=100)
qualifying_GFP = wf.qualifying_gfp(segmented_GFP,
in_channel='projected_GFP',
out_channel='qualifying_GFP')
average_GFP = wf.average_qualifying_value_per_region(qualifying_GFP,
in_channel=['pre_cell_labels', 'projected_GFP', 'qualifying_GFP'],
out_channel=['average_GFP', 'average_GFP_pad'])
max_GFP = wf.max_projection(max_mCherry,
in_channel='GFP',
out_channel='max_GFP')
stabilized_mCherry = wf.gamma_stabilize(max_GFP,
in_channel='max_mCherry',
floor_method='min',
alpha_clean=.5)
smoothed_mCherry = wf.smooth_2d(stabilized_mCherry,
in_channel='max_mCherry',
smoothing_px=.5)
mCherry_o_n_segmented = wf.robust_binarize(smoothed_mCherry,
in_channel='max_mCherry',
out_channel='max_mCherry_binary',
heterogeity_size=10,
feature_size=250)
running_render = examples.kristen_support.Kristen_render(
mCherry_o_n_segmented,
in_channel=[
'name pattern', 'group id', 'max_mCherry', 'max_mCherry_binary', 'GFP',
'mCherry'
],
out_channel='_',
output='Kristen_Transfection_B_and_C_GFP_analysis_results.csv',
save=True)
#
# Kristen_summary = rdr.Kristen_summarize_a(running_render, in_channel = ['name pattern', 'q_mean','q_median', 'q_std', 'nq_mean', 'nq_median', 'nq_std', 'slope', 'r2', 'p'],
stabilized_GFP = wf.gamma_stabilize(named_source, in_channel='GFP')
smoothed_GFP = wf.smooth(stabilized_GFP, in_channel='GFP')
stabilized_mCh = wf.gamma_stabilize(smoothed_GFP, in_channel='mCherry')
projected_GFP = wf.sum_projection(stabilized_mCh,
in_channel='GFP',
out_channel='projected_GFP')
projected_mCh = wf.max_projection(projected_GFP,
in_channel='mCherry',
out_channel='projected_mCh')
binarized_GFP = wf.robust_binarize(projected_mCh,
in_channel='projected_mCh',
out_channel='cell_tags')
segmented_GFP = wf.improved_watershed(
binarized_GFP,
in_channel=['cell_tags', 'projected_mCh'],
out_channel='pre_cell_labels')
qualifying_GFP = wf.qualifying_gfp(segmented_GFP,
in_channel='projected_GFP',
out_channel='qualifying_GFP')
average_GFP = wf.average_qualifying_value_per_region(
qualifying_GFP,
in_channel=['pre_cell_labels', 'projected_GFP', 'qualifying_GFP'],
out_channel=['average_GFP', 'average_GFP_pad'])
source = examples.akshay_support.Akshay_traverse(source_directory)
named_source = uf.name_channels(source, ['DAPI', 'p53', 'p21'])
stablilized_1 = wf.gamma_stabilize(named_source, in_channel='DAPI', floor_method='min', alpha_clean=.5)
smoothed_1 = wf.smooth_2d(stablilized_1, in_channel='DAPI', smoothing_px=.5) # corresponds to a pixel of 1500 nm
stablilized_2 = wf.gamma_stabilize(smoothed_1, in_channel='p53', floor_method='min', alpha_clean=.0)
smoothed_2 = wf.smooth_2d(stablilized_2, in_channel='p53', smoothing_px=.5)
stablilized_3 = wf.gamma_stabilize(smoothed_2, in_channel='p21', floor_method='5p', alpha_clean=.5)
smoothed_3 = wf.smooth_2d(stablilized_3, in_channel='p21', smoothing_px=.5)
binarized_nuclei = wf.robust_binarize(smoothed_3,
in_channel='DAPI',
out_channel=['nuclei'],
_dilation=0,
heterogeity_size=5, feature_size=50)
segmented_nuclei = wf.label_and_correct(binarized_nuclei,
in_channel=['nuclei', 'DAPI'],
out_channel='nuclei',
min_px_radius=15, min_intensity=20)
# p53 segmentation
p53_aq = wf.label_based_aq(segmented_nuclei,
in_channel=['nuclei', 'p53'],
out_channel=['av_p53', 'av_p53_pad'])
p53_o_n = wf.exclude_region(p53_aq,