Creating a standard starfish.wdl that can be run with any recipe file

workflows/wdl/iss_published/inputs.json

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+{
+  "Starfish.experiment": "https://d2nhj9g34unfro.cloudfront.net/browse/formatted/iss/20190506/experiment.json",
+  "Starfish.num_fovs": 15,
+  "Starfish.recipe_file": "https://raw.githubusercontent.com/spacetx/starfish/master/workflows/wdl/iss_published/recipe.py"
+}

workflows/wdl/iss_published/recipe.py

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+import starfish
+from starfish import FieldOfView
+from starfish.image import Filter
+from starfish.image import ApplyTransform, LearnTransform
+from starfish.spots import DetectSpots
+from starfish.types import Axes
+
+
+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")
+
+    masking_radius = 15
+    print("Filter WhiteTophat")
+    filt = Filter.WhiteTophat(masking_radius, is_volume=False)
+
+    filtered_imgs = filt.run(imgs, verbose=True, in_place=False)
+    filt.run(dots, verbose=True, in_place=True)
+    filt.run(nuclei, verbose=True, in_place=True)
+
+    print("Learning Transform")
+    learn_translation = LearnTransform.Translation(reference_stack=dots, axes=Axes.ROUND, upsampling=1000)
+    transforms_list = learn_translation.run(imgs.max_proj(Axes.CH, Axes.ZPLANE))
+
+    print("Applying transform")
+    warp = ApplyTransform.Warp()
+    registered_imgs = warp.run(filtered_imgs, transforms_list=transforms_list, in_place=False, verbose=True)
+
+    print("Detecting")
+    p = DetectSpots.BlobDetector(
+        min_sigma=1,
+        max_sigma=10,
+        num_sigma=30,
+        threshold=0.01,
+        measurement_type='mean',
+    )
+
+    intensities = p.run(registered_imgs, blobs_image=dots, blobs_axes=(Axes.ROUND, Axes.ZPLANE))
+
+    decoded = experiment.codebook.decode_per_round_max(intensities)
+    df = decoded.to_decoded_spots()
+    return df

workflows/wdl/iss_published/test_inputs.json

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+{
+  "Starfish.experiment": "https://d2nhj9g34unfro.cloudfront.net/browse/formatted/iss/20190506/experiment.json",
+  "Starfish.num_fovs": 2,
+  "Starfish.recipe_file": "https://raw.githubusercontent.com/spacetx/starfish/master/workflows/wdl/iss_published/recipe.py"
+}

workflows/wdl/iss_spaceTX/inputs.json

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+{
+  "Starfish.experiment": "https://d2nhj9g34unfro.cloudfront.net/xiaoyan_qian/ISS_human_HCA_07_MultiFOV/main_files/experiment.json",
+  "Starfish.num_fovs": 539,
+  "Starfish.recipe_file": "https://raw.githubusercontent.com/spacetx/starfish/master/workflows/wdl/iss_spaceTX/recipe.py"
+}

workflows/wdl/iss_spaceTX/recipe.py

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+import numpy as np
+import starfish
+from starfish.types import Axes
+
+
+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)
+
+    fov = experiment[fov_str]
+    imgs = fov.get_image(starfish.FieldOfView.PRIMARY_IMAGES)
+    dots = imgs.max_proj(Axes.CH)
+
+    # filter
+    filt = starfish.image.Filter.WhiteTophat(masking_radius=15, is_volume=False)
+    filtered_imgs = filt.run(imgs, verbose=True, in_place=False)
+    filt.run(dots, verbose=True, in_place=True)
+
+    # find threshold
+    tmp = dots.sel({Axes.ROUND:0, Axes.CH:0, Axes.ZPLANE:0})
+    dots_threshold = np.percentile(np.ravel(tmp.xarray.values), 50)
+
+    # find spots
+    p = starfish.spots.DetectSpots.BlobDetector(
+        min_sigma=1,
+        max_sigma=10,
+        num_sigma=30,
+        threshold=dots_threshold,
+        measurement_type='mean',
+    )
+
+    # blobs = dots; define the spots in the dots image, but then find them again in the stack.
+    intensities = p.run(filtered_imgs, blobs_image=dots, blobs_axes=(Axes.ROUND, Axes.ZPLANE))
+
+    # decode
+    decoded = experiment.codebook.decode_per_round_max(intensities)
+
+    # save results
+    df = decoded.to_decoded_spots()
+    return df

workflows/wdl/iss_spaceTX/test_inputs.json

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+{
+  "Starfish.experiment": "https://d2nhj9g34unfro.cloudfront.net/xiaoyan_qian/ISS_human_HCA_07_MultiFOV/main_files/experiment.json",
+  "Starfish.num_fovs": 2,
+  "Starfish.recipe_file": "https://raw.githubusercontent.com/spacetx/starfish/master/workflows/wdl/iss_spaceTX/recipe.py"
+}

workflows/wdl/merfish_published/inputs.json

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+{
+  "Starfish.experiment": "https://d2nhj9g34unfro.cloudfront.net/browse/formatted/MERFISH/20190511/experiment.json",
+  "Starfish.num_fovs": 495,
+  "Starfish.recipe_file": "https://raw.githubusercontent.com/spacetx/starfish/saxelrod-standard-wdl/workflows/wdl/merfish_published/recipe.py"
+}