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
# EPY: END markdown
# EPY: START code
from starfish.spots import SpotFinder
import warnings
# parameters to define the allowable gaussian sizes (parameter space)
min_sigma = 1
max_sigma = 10
num_sigma = 30
threshold = 0.01
p = SpotFinder.GaussianSpotDetector(
min_sigma=min_sigma,
max_sigma=max_sigma,
num_sigma=num_sigma,
threshold=threshold,
measurement_type='mean',
)
# detect triggers some numpy warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# blobs = dots; define the spots in the dots image, but then find them again in the stack.
blobs_image = dots.max_proj(Indices.ROUND, Indices.Z)
intensities = p.run(registered_image, blobs_image=blobs_image)
# EPY: END code
# EPY: START markdown
# The Encoder table is the hypothesized standardized file format for the output of a spot detector, and is the first output file format in the pipeline that is not an image or set of images