#-- INPUT FILE OPERATIONS ---------------------------------------------------#
# determine image filetype and open
print('Analyzing `' + p_img + '`...\n')
print('Genes: ' + ', '.join(genes))
img, img_name, mtree = mf.open_image(p_img)
if img_name[0].isdigit():
is_old_image = True
img_dapi = su.normalize_image(img[-1,:,:,:])
imgs_rna = [su.normalize_image(img[i,:,:,:]) for i in range(len(genes))]
if should_plot:
su.animate_zstacks(imgs_rna + [img_dapi], vmax=[0.2]*len(imgs_rna) + [1], gif_name=os.path.join(outdir, 'anim', img_name+'_channels.gif'))
# get voxel dimensions
dim_iter = mtree.find('Metadata').find('Scaling').find('Items').findall('Distance')
dims = {}
for dimension in dim_iter:
dim_id = dimension.get('Id')
dims.update({dim_id:float(dimension.find('Value').text)*1.E6}) # [um]
voxel_vol = dims['X'] * dims['Y'] * dims['Z'] # [um^3]
dims_xyz = np.array([dims['X'], dims['Y'], dims['Z']])
# get channel wavelengths
track_tree = mtree.find('Metadata').find('Experiment').find('ExperimentBlocks').find('AcquisitionBlock').find('MultiTrackSetup').findall('TrackSetup')
tracks = []
for track in track_tree:
name = track.get('Name')
s3_param = [[1, 0.75*t_spots[i]], [2, 0.75*t_spots[i]]]
print('Identifying spots...')
bw = dot_3d_wrapper(img_rna_smooth, s3_param)
# watershed
minArea = 4
Mask = morphology.remove_small_objects(bw>0, min_size=minArea, connectivity=1, in_place=False)
labeled_mask = measure.label(Mask)
print('Performing watershed segmentation...')
peaks = feature.peak_local_max(imgs_rna[i],labels=labeled_mask, min_distance=2, indices=False)
Seed = morphology.binary_dilation(peaks, selem=morphology.ball(1))
Watershed_Map = -1*distance_transform_edt(bw)
seg = morphology.watershed(Watershed_Map, measure.label(Seed), mask=Mask, watershed_line=True)
seg = morphology.remove_small_objects(seg>0, min_size=minArea, connectivity=1, in_place=False)
print('Exporting mask...')
outname = img_name + '_mask^' + g + '.tiff'
p_out = os.path.join(outdir, 'masks', outname)
seg = seg > 0
out=seg.astype(np.uint8)
out[out>0]=255
writer = OmeTiffWriter(p_out, overwrite_file=True)
writer.save(out)
# output animation
print('Exporting animation...')
anim_name = img_name + '_mask^' + g + '.gif'
su.animate_zstacks([imgs_rna[i].transpose(1,2,0), seg.astype(int).transpose(1,2,0)], vmax=[0.2, 1], gif_name=os.path.join(outdir, 'anim', anim_name))
if not p_blobs:
p_blobs = []
for g in genes:
p_blobs.append(
os.path.join(outdir, 'masks', img_name + '_mask^' + g + '.tiff'))
if img_name[0].isdigit():
is_old_image = True
img_dapi = su.normalize_image(img[-1, :, :, :])
imgs_rna = [su.normalize_image(img[i, :, :, :]) for i in range(len(genes))]
imgs_rna_unnormed = [img[i, :, :, :] for i in range(len(genes))]
if should_plot:
su.animate_zstacks(imgs_rna + [img_dapi],
vmax=[0.2] * len(imgs_rna) + [1],
gif_name=os.path.join(outdir, 'anim',
img_name + '_channels.gif'))
# get voxel dimensions
dim_iter = mtree.find('Metadata').find('Scaling').find('Items').findall(
'Distance')
dims = {}
for dimension in dim_iter:
dim_id = dimension.get('Id')
dims.update({dim_id: float(dimension.find('Value').text) * 1.E6}) # [um]
voxel_vol = dims['X'] * dims['Y'] * dims['Z'] # [um^3]
dims_xyz = np.array([dims['X'], dims['Y'], dims['Z']])
# get channel wavelengths
track_tree = mtree.find('Metadata').find('Experiment').find(
'ExperimentBlocks').find('AcquisitionBlock').find(
# split channels
img_dapi = su.normalize_image(img[-1,:,:,:])
print('DAPI: ' + str(tracks[-1]))
if fish_channel:
try:
img_rna = su.normalize_image(img[tracks.index(int(fish_channel)),:,:,:])
print('FISH: ' + fish_channel)
except ValueError:
raise ValueError('FISH channel not recognized. Must match one of the above tracks.')
else:
img_rna = su.normalize_image(img[0,:,:,:])
print('FISH: ' + str(tracks[0]))
if should_plot:
su.animate_zstacks([img_dapi, np.clip(img_rna, a_min=0, a_max=np.amax(img_rna)/5.)], titles=['DAPI', 'FISH, gain=5'], gif_name=os.path.join(outdir, 'anim', img_name+'_channels.gif'))
#-- FIBER SEGMENTATION ------------------------------------------------------#
print('\nSegmenting fiber from background...')
img_fiber_only = mf.threshold_fiber(img_rna, t_fiber=t_fiber, verbose=True)
# calculate volume of fiber
volume = np.sum(img_fiber_only) * voxel_vol
print('\nfiber = ' + str(float(100.*np.sum(img_fiber_only))/img_fiber_only.size) + '% of FOV')
print('volume = ' + str(volume) + ' um^3')
if should_plot:
su.animate_zstacks([np.clip(img_rna, a_min=0, a_max=np.amax(img_rna)/5.), img_fiber_only], titles=['FISH, gain=5', 'fiber prediction'], gif_name=os.path.join(outdir, 'anim', img_name+'_fiber.gif'))
# construct allowed region for RNA mobility
mask_allowed = np.logical_and(mask_fiber.astype(bool),
np.logical_not(mask_nuc_eroded).astype(bool))
labeled_mask_allowed, n_regions = morphology.label(mask_allowed,
return_num=True)
voxel_counts = []
for l in range(1, n_regions + 1):
voxel_counts.append(np.count_nonzero(labeled_mask_allowed == l))
mask_allowed = (labeled_mask_allowed == np.argmax(voxel_counts) + 1)
tifffile.imwrite(os.path.join(outdir, img_name + '_allowed_region.tiff'),
data=mask_allowed.transpose(2, 0, 1).astype(np.uint8) * 255,
compress=6,
photometric='minisblack')
su.animate_zstacks([mask_fiber, mask_nuc, mask_allowed],
titles=['fiber', 'nuclei', 'allowed'],
gif_name=os.path.join(outdir,
img_name + '_masks^' + gene + '.gif'))
# create spawning probability matrix
labeled_mask_nuc, n_nuc = morphology.label(mask_nuc, return_num=True)
labeled_mask_nuc[mask_nuc_eroded == 1] = 0
labeled_mask_nuc[mask_allowed == 0] = 0
voxels_by_label = {
l: np.count_nonzero(labeled_mask_nuc == l)
for l in range(1, n_nuc + 1)
}
spawn_probs = np.zeros_like(mask_nuc, dtype=np.float32)
sp_x = []
sp_P = []
for l in range(1, n_nuc + 1):
print('Genes: ' + ', '.join(genes))
img, img_name, mtree = mf.open_image(p_img)
if transpose:
img = img.transpose(0, 2, 1, 3)
if img_name[0].isdigit():
is_old_image = True
img_dapi = su.normalize_image(img[-1, :, :, :])
imgs_rna = [su.normalize_image(img[i, :, :, :]) for i in range(len(genes))]
if should_plot:
su.animate_zstacks(imgs_rna + [img_dapi],
vmax=[0.2] * len(imgs_rna) + [1],
gif_name=os.path.join(outdir, 'anim',
img_name + '_channels.gif'))
# get voxel dimensions
dim_iter = mtree.find('Metadata').find('Scaling').find('Items').findall(
'Distance')
dims = {}
for dimension in dim_iter:
dim_id = dimension.get('Id')
dims.update({dim_id: float(dimension.find('Value').text) * 1.E6}) # [um]
voxel_vol = dims['X'] * dims['Y'] * dims['Z'] # [um^3]
dims_xyz = np.array([dims['X'], dims['Y'], dims['Z']])
with open(os.path.join(outdir, img_name + '_dims.csv'), 'w') as outfile:
writer = csv.writer(outfile)
writer.writerows([['x', dims['X']], ['y', dims['Y']], ['z', dims['Z']]])