Single Field of View for sequential single-molecule FISH processed in 3d

This notebook walks through a work flow that analyzes one field of view of a mouse gene panel from the Allen Institute for Cell Science, using the starfish package.

This example processes an experiment with a single round from a single field of view of sequential smFISH data taken from mouse primary visual cortex. These data are unpublished, and were kindly contributed by the Allen Institute for Brain Science as a part of the SpaceTx consortium project.

The data consist of 45 images from 1 round, 1 channels, and 33 z-planes. Each image is (2048, 2048) (y, x). There are no test data.

from typing import Optional, Tuple
from IPython import get_ipython

import starfish
from starfish import FieldOfView, DecodedIntensityTable
from starfish.types import TraceBuildingStrategies

# equivalent to %gui qt
ipython = get_ipython()
ipython.magic("gui qt5")

Define image filters

The 3d smFISH workflow run by the Allen runs a bandpass filter to remove high and low frequency signal and blurs over z with a 1-pixel gaussian to smooth the signal over the z-axis.

low-intensity signal is (stringently) clipped from the images before and after these filters.

# bandpass filter to remove cellular background and camera noise
bandpass = starfish.image.Filter.Bandpass(lshort=.5, llong=7, threshold=0.0)

# gaussian blur to smooth z-axis
glp = starfish.image.Filter.GaussianLowPass(
    sigma=(1, 0, 0),

# pre-filter clip to remove low-intensity background signal
clip1 = starfish.image.Filter.Clip(p_min=50, p_max=100)

# post-filter clip to eliminate all but the highest-intensity peaks
clip2 = starfish.image.Filter.Clip(p_min=99, p_max=100, is_volume=True)

Define a spot detection method

Spots are detected using a spot finder based on trackpy’s locate method, which identifies local intensity maxima, and spots are matched to the gene they represent by looking them up in a codebook that records which (round, channel) matches which gene target.

tlmpf = starfish.spots.FindSpots.TrackpyLocalMaxPeakFinder(
    spot_diameter=5,  # must be odd integer
    max_size=2,  # this is max radius
    percentile=10,  # this is irrelevant when min_mass, spot_diameter, and max_size are set properly

Construct the pipeline

def processing_pipeline(
    experiment: starfish.Experiment,
    fov_name: str,
    n_processes: Optional[int]=None
) -> Tuple[starfish.ImageStack, starfish.IntensityTable]:
    """Process a single field of view of an experiment

    experiment : starfish.Experiment
        starfish experiment containing fields of view to analyze
    fov_name : str
        name of the field of view to process
    n_processes : int

    starfish.IntensityTable :
        decoded IntensityTable containing spots matched to the genes they are hybridized against

    all_intensities = list()
    codebook = experiment.codebook

    print("Loading images...")
    images = enumerate(experiment[fov_name].get_images(FieldOfView.PRIMARY_IMAGES))

    decoder = starfish.spots.DecodeSpots.PerRoundMaxChannel(

    for image_number, primary_image in images:
        print(f"Filtering image {image_number}...")
        filter_kwargs = dict(
        ), **filter_kwargs), **filter_kwargs), **filter_kwargs), **filter_kwargs)

        print("Calling spots...")
        spots =
        print("Decoding spots...")
        decoded_intensities =

    decoded = DecodedIntensityTable.concatenate_intensity_tables(all_intensities)
    decoded = decoded[decoded["total_intensity"] > .025]

    return primary_image, decoded

Load data, run pipeline, display results

experiment =

image, intensities = processing_pipeline(experiment, fov_name='fov_001')

# uncomment the below line to visualize the output with the spot calls.
# viewer = starfish.display(image, intensities)

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