def build_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--profile",
action="store_true",
help="enable profiling")
parser.add_argument("--noop",
help=argparse.SUPPRESS,
dest="starfish_command",
action="store_const",
const=noop)
subparsers = parser.add_subparsers(dest="starfish_command")
Registration._add_to_parser(subparsers)
Filter._add_to_parser(subparsers)
SpotFinder.add_to_parser(subparsers)
Segmentation._add_to_parser(subparsers)
TargetAssignment.add_to_parser(subparsers)
Decoder.add_to_parser(subparsers)
show_group = subparsers.add_parser("show")
show_group.add_argument("in_json", type=FsExistsType())
show_group.add_argument("--sz", default=10, type=int, help="Figure size")
show_group.set_defaults(starfish_command=show)
build_group = subparsers.add_parser("build")
BuilderCli.add_to_parser(build_group)
validate_group = subparsers.add_parser("validate")
ValidateCli.add_to_parser(validate_group)
return parser
def iss_pipeline(fov, codebook):
primary_image = fov[starfish.FieldOfView.PRIMARY_IMAGES]
# 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.BlobDetector(
min_sigma=1,
max_sigma=10,
num_sigma=30,
threshold=0.01,
measurement_type='mean',
)
mp = fov['dots'].max_proj(Indices.ROUND, Indices.Z)
mp_numpy = mp._squeezed_numpy(Indices.ROUND, Indices.Z)
intensities = p.run(filtered, blobs_image=mp_numpy)
# decode the pixel traces using the codebook
decoded = codebook.decode_per_round_max(intensities)
# segment cells
seg = Segmentation.Watershed(
nuclei_threshold=.16,
input_threshold=.22,
min_distance=57,
)
label_image = seg.run(primary_image, fov['nuclei'])
# assign spots to cells
ta = TargetAssignment.Label()
assigned = ta.run(label_image, decoded)
return assigned, label_image
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 markdown
#For each imaging round, the max projection across color channels should look like the dots stain.
#Below, this computes the max projection across the color channels of an imaging round and learns the linear transformation to maps the resulting image onto the dots image.
#
#The Fourier shift registration approach can be thought of as maximizing the cross-correlation of two images.
#
#In the below table, Error is the minimum mean-squared error, and shift reports changes in x and y dimension.
# EPY: END markdown
# EPY: START code
from starfish.image import Registration
registration = Registration.FourierShiftRegistration(upsampling=1000,
reference_stack=dots,
verbose=True)
registered_image = registration.run(primary_image, in_place=False)
# EPY: END code
# EPY: START markdown
### Use spot-detector to create 'encoder' table for standardized input to decoder
# EPY: END markdown
# EPY: START markdown
#Each pipeline exposes an encoder that translates an image into spots with intensities. This approach uses a Gaussian spot detector.
# EPY: END markdown
# EPY: START code
from starfish.spots import SpotFinder
import warnings