def test_target_assignment_point_in_poly_2d_points_outside_of_cells():
"""Create a set of intensities where no points fall into cells"""
n = 10
diagonal_intensities, regions = bifurcated_regions(n)
diagonal_intensities[
Indices.X.value] += 50 # now all x-values lie outside cell regions
ta = TargetAssignment.PointInPoly2D()
assigned_intensities = ta.run(diagonal_intensities, regions)
assert np.array_equal(assigned_intensities[Features.CELL_ID].values,
np.full(n, fill_value=None, dtype=object))
def test_target_assignment_point_in_poly_2d_all_points_in_cells():
"""Create a set of intensities where all points lie inside the defined cell regions"""
diagonal_intensities, regions = bifurcated_regions(10)
ta = TargetAssignment.PointInPoly2D()
assigned_intensities = ta.run(diagonal_intensities, regions)
target_counts = pd.Series(*np.unique(
assigned_intensities[Features.CELL_ID], return_counts=True)[::-1])
assert target_counts[0] == 5
assert target_counts[1] == 5
def iss_pipeline(fov, codebook):
primary_image = fov.primary_image
# register the raw images
registration = Registration.FourierShiftRegistration(
upsampling=1000,
reference_stack=fov['dots']
)
registered = registration.run(primary_image, in_place=False)
# filter raw data
masking_radius = 15
filt = Filter.WhiteTophat(masking_radius, is_volume=False)
filtered = filt.run(registered, verbose=True, in_place=False)
# detect spots using laplacian of gaussians approach
p = SpotFinder.GaussianSpotDetector(
min_sigma=1,
max_sigma=10,
num_sigma=30,
threshold=0.01,
measurement_type='mean',
)
blobs_image = fov['dots'].max_proj(Indices.ROUND, Indices.Z)
intensities = p.run(filtered, blobs_image=blobs_image)
# decode the pixel traces using the codebook
decoded = codebook.decode_per_round_max(intensities)
# segment cells
seg = Segmentation.Watershed(
dapi_threshold=.16,
input_threshold=.22,
min_distance=57,
)
regions = seg.run(primary_image, fov['nuclei'])
# assign spots to cells
ta = TargetAssignment.PointInPoly2D()
assigned = ta.run(decoded, regions)
return assigned, regions