def process_fov(fov: FieldOfView, codebook: Codebook) -> DecodedIntensityTable:
"""Process a single field of view of ISS data
Parameters
----------
fov : FieldOfView
the field of view to process
codebook : Codebook
the Codebook to use for decoding
Returns
-------
DecodedSpots :
tabular object containing the locations of detected spots.
"""
# note the structure of the 5D tensor containing the raw imaging data
imgs = fov.get_image(FieldOfView.PRIMARY_IMAGES)
dots = fov.get_image("dots")
nuclei = fov.get_image("nuclei")
print("Learning Transform")
learn_translation = LearnTransform.Translation(reference_stack=dots,
axes=Axes.ROUND,
upsampling=1000)
transforms_list = learn_translation.run(
imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max"))
print("Applying transform")
warp = ApplyTransform.Warp()
registered_imgs = warp.run(imgs,
transforms_list=transforms_list,
verbose=True)
print("Filter WhiteTophat")
filt = Filter.WhiteTophat(masking_radius=15, is_volume=False)
filtered_imgs = filt.run(registered_imgs, verbose=True)
filt.run(dots, verbose=True, in_place=True)
filt.run(nuclei, verbose=True, in_place=True)
print("Detecting")
detector = FindSpots.BlobDetector(
min_sigma=1,
max_sigma=10,
num_sigma=30,
threshold=0.01,
measurement_type='mean',
)
dots_max = dots.reduce((Axes.ROUND, Axes.ZPLANE),
func="max",
module=FunctionSource.np)
spots = detector.run(image_stack=filtered_imgs, reference_image=dots_max)
print("Decoding")
decoder = DecodeSpots.PerRoundMaxChannel(codebook=codebook)
decoded = decoder.run(spots=spots)
return decoded
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.reduce({Axes.CH, Axes.ZPLANE}, func="max"))
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)
bd = FindSpots.BlobDetector(
min_sigma=1,
max_sigma=10,
num_sigma=30,
threshold=0.01,
measurement_type='mean',
)
# detect spots using laplacian of gaussians approach
dots_max = fov.get_image('dots').reduce((Axes.ROUND, Axes.ZPLANE),
func="max",
module=FunctionSource.np)
# locate spots in a reference image
spots = bd.run(reference_image=dots_max, image_stack=filtered)
# decode the pixel traces using the codebook
decoder = DecodeSpots.PerRoundMaxChannel(codebook=codebook)
decoded = decoder.run(spots=spots)
# segment cells
seg = Segment.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 = AssignTargets.Label()
assigned = ta.run(label_image, decoded)
return assigned, label_image
masking_radius = 15
filt = Filter.WhiteTophat(masking_radius, is_volume=False)
filt.run(imgs, in_place=True)
filt.run(dots, in_place=True)
# register primary images to reference round
learn_translation = LearnTransform.Translation(reference_stack=dots,
axes=Axes.ROUND,
upsampling=1000)
transforms_list = learn_translation.run(
imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max"))
warp = ApplyTransform.Warp()
warp.run(imgs, transforms_list=transforms_list, in_place=True)
# run blob detector on dots (reference image with every spot)
bd = FindSpots.BlobDetector(
min_sigma=1,
max_sigma=10,
num_sigma=30,
threshold=0.01,
measurement_type='mean',
)
dots_max = dots.reduce((Axes.ROUND, Axes.ZPLANE), func="max")
spots = bd.run(image_stack=imgs, reference_image=dots_max)
# Decode spots with PerRoundMaxChannel
from starfish.spots import DecodeSpots
decoder = DecodeSpots.PerRoundMaxChannel(
codebook=experiment.codebook,
trace_building_strategy=TraceBuildingStrategies.EXACT_MATCH)
decoded_intensities = decoder.run(spots=spots)
def decode_spots(codebook, spots):
decoder = DecodeSpots.PerRoundMaxChannel(codebook=codebook)
return decoder.run(spots=spots)
# EPY: START code
import warnings
from starfish.spots import FindSpots, DecodeSpots
bd = FindSpots.BlobDetector(
min_sigma=1,
max_sigma=10,
num_sigma=30,
threshold=0.01,
measurement_type='mean',
)
dots_max = dots.reduce((Axes.ROUND, Axes.ZPLANE), func=FunctionSource.np("max"))
spots = bd.run(image_stack=registered_imgs, reference_image=dots_max)
decoder = DecodeSpots.PerRoundMaxChannel(codebook=experiment.codebook)
decoded = decoder.run(spots=spots)
# Besides house keeping genes, VIM and HER2 should be most highly expessed, which is consistent here.
genes, counts = np.unique(decoded.loc[decoded[Features.PASSES_THRESHOLDS]][Features.TARGET], return_counts=True)
table = pd.Series(counts, index=genes).sort_values(ascending=False)
table
# EPY: END code
# EPY: START markdown
### Segment Cells and create Cell by Gene Expression Matrix
#
#After calling spots and decoding their gene information, cells must be segmented to assign genes to cells. This paper used a seeded watershed approach to segment the cells, which we also use here.
# EPY: END markdown
# EPY: START code
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
min_obj_area=args.small_peak_min,
max_obj_area=args.small_peak_max,
min_num_spots_detected=2500,
is_volume=False,
verbose=False)
lmp_big = FindSpots.LocalMaxPeakFinder(min_distance=args.big_peak_dist,
stringency=0,
min_obj_area=args.big_peak_min,
max_obj_area=args.big_peak_max,
min_num_spots_detected=2500,
is_volume=False,
verbose=False)
sd = Codebook.synthetic_one_hot_codebook(n_round=1,
n_channel=1,
n_codes=1)
decoder = DecodeSpots.PerRoundMaxChannel(codebook=sd)
block_dim = int(max(imarr.shape) * args.block_dim_fraction)
SpotCoords = np.zeros((0, 2), dtype=np.int64)
for i in range(
0, imarr.shape[-2] - 1, block_dim
): # subtracting 1 from range because starfish breaks with x or y axis size of 1
for j in range(0, imarr.shape[-1] - 1, block_dim):
imgs = ImageStack.from_numpy(imarr[:, :, :, i:i + block_dim,
j:j + block_dim])
imgs = bandpass.run(imgs).reduce({Axes.ZPLANE}, func="max")
spots = lmp_small.run(imgs)
decoded_intensities = decoder.run(spots=spots)
spot_coords_small = np.stack([
decoded_intensities[Axes.Y.value],
decoded_intensities[Axes.X.value]
]).T
# EPY: START code
from starfish.spots import DecodeSpots, FindSpots
from starfish.types import TraceBuildingStrategies
lmp = FindSpots.LocalMaxPeakFinder(
min_distance=6,
stringency=0,
min_obj_area=6,
max_obj_area=600,
is_volume=True
)
spots = lmp.run(mp)
decoder = DecodeSpots.PerRoundMaxChannel(codebook=experiment.codebook,
trace_building_strategy=TraceBuildingStrategies.SEQUENTIAL)
decoded_intensities = decoder.run(spots=spots)
# EPY: END code
# EPY: START markdown
### Compare to pySMFISH peak calls
# EPY: END markdown
# EPY: START markdown
#The Field of view that we've used for the test data corresponds to Aldoc, imaged in round one, in position 33. We've also packaged the results from the osmFISH publication for this target to demonstrate that starfish is capable of recovering the same results.
# EPY: END markdown
# EPY: START code
def load_results(pickle_file):
with open(pickle_file, "rb") as f:
return pickle.load(f)
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)
bd = FindSpots.BlobDetector(min_sigma=1,
max_sigma=8,
num_sigma=10,
threshold=np.percentile(
np.ravel(stack.xarray.values), 95),
exclude_border=2)
spots = bd.run(scaled)
###################################################################################################
# Decode spots
# ------------
# Next, spots are decoded. There is really no good way to display 3-d spot detection in 2-d planes,
# so we encourage you to grab this notebook and uncomment the below lines.
from starfish.spots import DecodeSpots
from starfish.types import TraceBuildingStrategies
decoder = DecodeSpots.PerRoundMaxChannel(
codebook=experiment.codebook,
anchor_round=0,
search_radius=10,
trace_building_strategy=TraceBuildingStrategies.NEAREST_NEIGHBOR)
decoded = decoder.run(spots=spots)
decode_mask = decoded['target'] != 'nan'
# %gui qt
# viewer = starfish.display(stack, decoded[decode_mask], radius_multiplier=2, mask_intensities=0.1)
def find_spots(input_path,
output_path,
intensity_percentile=99.995,
filter_width=2,
small_peak_min=4,
small_peak_max=100,
big_peak_min=25,
big_peak_max=10000,
small_peak_dist=2,
big_peak_dist=0.75,
block_dim_fraction=0.25,
spot_pad_pixels=2,
keep_existing=False):
"""
Find and keep only spots from stitched images.
"""
image_stack = imageio.volread(input_path)
print(image_stack.shape)
thresholded_image = np.copy(image_stack)
_, height, width = image_stack.shape
threshold = np.percentile(thresholded_image, intensity_percentile)
thresholded_image[thresholded_image > threshold] = threshold + (
np.log(thresholded_image[thresholded_image > threshold] - threshold) /
np.log(1.1)).astype(thresholded_image.dtype)
#May need to fiddle with the sigma parameters in each step, depending on the image.
#High Pass Filter (Background Subtraction)
gaussian_high_pass = Filter.GaussianHighPass(sigma=(1, filter_width,
filter_width),
is_volume=True)
# enhance brightness of spots
laplace_filter = Filter.Laplace(sigma=(0.2, 0.5, 0.5), is_volume=True)
local_max_peakfinder_small = FindSpots.LocalMaxPeakFinder(
min_distance=small_peak_dist,
stringency=0,
min_obj_area=small_peak_min,
max_obj_area=small_peak_max,
min_num_spots_detected=2500,
is_volume=True,
verbose=True)
local_max_peakfinder_big = FindSpots.LocalMaxPeakFinder(
min_distance=big_peak_dist,
stringency=0,
min_obj_area=big_peak_min,
max_obj_area=big_peak_max,
min_num_spots_detected=2500,
is_volume=True,
verbose=True)
synthetic_codebook = Codebook.synthetic_one_hot_codebook(n_round=1,
n_channel=1,
n_codes=1)
decoder = DecodeSpots.PerRoundMaxChannel(codebook=synthetic_codebook)
block_dimension = int(max(thresholded_image.shape) * block_dim_fraction)
spot_coordinates = np.zeros((0, 2), dtype=np.int64)
# Finding spots by block_dimension x block_dimension size blocks
# We skip the blocks at the edges with the - 1 (TODO: pad to full block size)
for row in range(0, height - 1, block_dimension):
for column in range(0, width - 1, block_dimension):
# Cutout block and expand dimensions for channel and round
block = thresholded_image[np.newaxis, np.newaxis, :,
row:row + block_dimension,
column:column + block_dimension]
images = ImageStack.from_numpy(block)
high_pass_filtered = gaussian_high_pass.run(images,
verbose=False,
in_place=False)
laplace = laplace_filter.run(high_pass_filtered,
in_place=False,
verbose=False)
small_spots = local_max_peakfinder_small.run(
laplace.reduce({Axes.ZPLANE}, func="max"))
decoded_intensities = decoder.run(spots=small_spots)
small_spot_coords = np.stack([
decoded_intensities[Axes.Y.value],
decoded_intensities[Axes.X.value]
]).T
big_spots = local_max_peakfinder_big.run(
laplace.reduce({Axes.ZPLANE}, func="max"))
decoded_intensities = decoder.run(spots=big_spots)
big_spot_coords = np.stack([
decoded_intensities[Axes.Y.value],
decoded_intensities[Axes.X.value]
]).T
all_spot_coords = np.vstack([small_spot_coords, big_spot_coords])
all_spot_coords += (row, column)
spot_coordinates = np.vstack([spot_coordinates, all_spot_coords])
# Copying over only non-zero pixels
image_spots = np.zeros((height, width), dtype=np.uint16)
for spot_coordinate in spot_coordinates:
spot_column, spot_row = spot_coordinate
for row in range(max(0, spot_column - spot_pad_pixels),
min(spot_column + spot_pad_pixels + 1, height)):
for column in range(max(0, spot_row - spot_pad_pixels),
min(spot_row + spot_pad_pixels + 1, width)):
# Max projecting over z-stack
image_spots[row, column] = image_stack[:, row, column].max(0)
imageio.imsave(output_path, image_spots)
return image_spots
# run blob detector on dots (reference image with every spot)
bd = FindSpots.BlobDetector(
min_sigma=1,
max_sigma=3,
num_sigma=10,
threshold=0.01,
measurement_type='mean',
)
spots = bd.run(image_stack=imgs, reference_image=dots)
# Decode spots with MetricDistance set to loose parameters
from starfish.spots import DecodeSpots
decoder = DecodeSpots.MetricDistance(
codebook=experiment.codebook,
max_distance=1,
min_intensity=1,
metric='euclidean',
norm_order=2,
trace_building_strategy=TraceBuildingStrategies.EXACT_MATCH)
decoded_intensities = decoder.run(spots=spots)
# Build IntensityTable with same TraceBuilder as was used in MetricDistance
from starfish.core.spots.DecodeSpots.trace_builders import build_spot_traces_exact_match
intensities = build_spot_traces_exact_match(spots)
# Get vector magnitudes
norm_intensities, vector_magnitude = experiment.codebook._normalize_features(
intensities, norm_order=2)
# Get distances
distances = decoded_intensities.to_decoded_dataframe(
).data['distance'].to_numpy()
# Plot histogram
def process_fov(field_num: int, experiment_str: str):
"""Process a single field of view of ISS data
Parameters
----------
field_num : int
the field of view to process
experiment_str : int
path of experiment json file
Returns
-------
DecodedSpots :
tabular object containing the locations of detected spots.
"""
fov_str: str = f"fov_{int(field_num):03d}"
# load experiment
experiment = starfish.Experiment.from_json(experiment_str)
print(f"loading fov: {fov_str}")
fov = experiment[fov_str]
# note the structure of the 5D tensor containing the raw imaging data
imgs = fov.get_image(FieldOfView.PRIMARY_IMAGES)
dots = fov.get_image("dots")
nuclei = fov.get_image("nuclei")
print("Learning Transform")
learn_translation = LearnTransform.Translation(reference_stack=dots,
axes=Axes.ROUND,
upsampling=1000)
transforms_list = learn_translation.run(
imgs.reduce({Axes.CH, Axes.ZPLANE}, func="max"))
print("Applying transform")
warp = ApplyTransform.Warp()
registered_imgs = warp.run(imgs,
transforms_list=transforms_list,
in_place=True,
verbose=True)
print("Filter WhiteTophat")
filt = Filter.WhiteTophat(masking_radius=15, is_volume=False)
filtered_imgs = filt.run(registered_imgs, verbose=True, in_place=True)
filt.run(dots, verbose=True, in_place=True)
filt.run(nuclei, verbose=True, in_place=True)
print("Detecting")
detector = FindSpots.BlobDetector(
min_sigma=1,
max_sigma=10,
num_sigma=30,
threshold=0.01,
measurement_type='mean',
)
dots_max_projector = Filter.Reduce((Axes.ROUND, Axes.ZPLANE),
func=FunctionSource.np("max"))
dots_max = dots_max_projector.run(dots)
spots = detector.run(image_stack=filtered_imgs, reference_image=dots_max)
decoder = DecodeSpots.PerRoundMaxChannel(codebook=experiment.codebook)
decoded = decoder.run(spots=spots)
df = decoded.to_decoded_dataframe()
return df
