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.get_image(starfish.FieldOfView.PRIMARY_IMAGES)
# register the raw image
learn_translation = LearnTransform.Translation(
reference_stack=fov.get_image('dots'), axes=Axes.ROUND, upsampling=100)
transforms_list = learn_translation.run(
primary_image.max_proj(Axes.CH, Axes.ZPLANE))
warp = ApplyTransform.Warp()
registered = warp.run(primary_image,
transforms_list=transforms_list,
in_place=False,
verbose=True)
# 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',
)
intensities = p.run(filtered,
blobs_image=fov.get_image('dots'),
blobs_axes=(Axes.ROUND, Axes.ZPLANE))
# 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.get_image('dots'))
# 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[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: START code
from starfish.image import Segmentation
dapi_thresh = .16 # binary mask for cell (nuclear) locations
stain_thresh = .22 # binary mask for overall cells // binarization of stain
min_dist = 57
registered_mp = registered_image.max_proj(Axes.CH, Axes.ZPLANE)
registered_mp_numpy = registered_mp._squeezed_numpy(Axes.CH, Axes.ZPLANE)
stain = np.mean(registered_mp_numpy, axis=0)
stain = stain / stain.max()
nuclei = nuclei.max_proj(Axes.ROUND, Axes.CH, Axes.ZPLANE)
nuclei_numpy = nuclei._squeezed_numpy(Axes.ROUND, Axes.CH, Axes.ZPLANE)
seg = Segmentation.Watershed(nuclei_threshold=dapi_thresh,
input_threshold=stain_thresh,
min_distance=min_dist)
label_image = seg.run(registered_image, nuclei)
seg.show()
# EPY: END code
# EPY: START markdown
#### Assign spots to cells and create cell x gene count matrix
# EPY: END markdown
# EPY: START code
from starfish.spots import TargetAssignment
al = TargetAssignment.Label()
labeled = al.run(label_image, decoded)
# EPY: END code