4. Assign genes to cells
5. Calculate area of non-stroma tissue, add up each type of gene, normalize gene density.
"""
gene_counts_across_fovs = []
for fov in exp.fovs():
# 1 - already completed
# 2: Filter images and project Zplanes
img_stack = next(exp[fov].get_images(FieldOfView.PRIMARY_IMAGES))
img_stack_f1 = ghp.run(img_stack, in_place=False)
img_stack_f2 = glp.run(img_stack_f1, in_place=False)
img_proj_z = img_stack_f2.reduce({Axes.ZPLANE}, func='max')
# 2: Find Spots
spots = SpotFinder.run(img_proj_z)
decoder = DecodeSpots.SimpleLookupDecoder(codebook=codebook)
decoded_intensities = decoder.run(spots=spots)
# 3:
path = root_path + 'ilastik/classified_fovs/' + exp_name + fov + '.tif'
mask, label_image = produceMaskFromTif(path, img_stack)
# 4:
al = AssignTargets.Label()
labeled = al.run(mask, decoded_intensities)
cg = labeled.to_expression_matrix()
# 5:
STROMA_ID = 2
NON_STROMA_ID = 1
experiment = starfish.data.allen_smFISH(use_test_data=True)
image = experiment["fov_001"].get_image(FieldOfView.PRIMARY_IMAGES)
bandpass = Filter.Bandpass(lshort=.5, llong=7, threshold=0.0)
glp = Filter.GaussianLowPass(sigma=(1, 0, 0), is_volume=True)
clip1 = Filter.Clip(p_min=50, p_max=100, level_method=Levels.SCALE_BY_CHUNK)
clip2 = Filter.Clip(p_min=99,
p_max=100,
is_volume=True,
level_method=Levels.SCALE_BY_CHUNK)
tlmpf = starfish.spots.FindSpots.TrackpyLocalMaxPeakFinder(
spot_diameter=5,
min_mass=0.02,
max_size=2,
separation=7,
noise_size=0.65,
preprocess=False,
percentile=10,
verbose=True,
is_volume=True,
)
clip1.run(image, in_place=True)
bandpass.run(image, in_place=True)
glp.run(image, in_place=True)
clip2.run(image, in_place=True)
spots = tlmpf.run(image)
# Decode spots with SimpleLookupDecoder
from starfish.spots import DecodeSpots
decoder = DecodeSpots.SimpleLookupDecoder(codebook=experiment.codebook)
decoded_intensities = decoder.run(spots=spots)