Assigning Spots to CellsΒΆ

In a starfish pipeline, creating a single cell gene expression matrix is a two step process. It requires a DecodedIntensityTable with features mapped to targets and a BinaryMaskCollection with cell masks that were found by segmenting cells in the FOV.

The first step is to run Label to label each feature in the DecodedIntensityTable with the cell_id of the cell mask the feature is located in. Features that are not within the boundaries of any cell mask are labeled nan in the cell_id column. Features that are in multiple cell masks (i.e. the cell masks overlap) will be assigned to the last mask in the BinaryMaskCollection that the feature is found in.

The second step is to transform the DecodedIntensityTable into an ExpressionMatrix with to_expression_matrix(). At this stage, additional cell metadata can be added. The ExpressionMatrix can then be saved in various popular formats for single-cell RNAseq analysis packages.

This tutorial demonstrates how to use decoded MERFISH spots and the manually segmented cell masks to assign spots to cells and create a gene expression matrix. It then provides a couple examples how cell metadata can be added to the ExpressionMatrix and how the matrix can be saved for loading into other analysis tools.

# Load MERFISH data
import os
import numpy as np
from copy import deepcopy
from starfish import BinaryMaskCollection, data
from starfish.core.experiment.experiment import FieldOfView
from starfish.image import Filter
from starfish.spots import DetectPixels, AssignTargets
from starfish.types import Axes, Features, Levels

experiment = data.MERFISH()
fov = experiment["fov_000"]
imgs = experiment.fov().get_image(FieldOfView.PRIMARY_IMAGES)
dapi = fov.get_image("nuclei")  # nuclei

# filter and deconvolve data
ghp = Filter.GaussianHighPass(sigma=3)
dpsf = Filter.DeconvolvePSF(num_iter=15, sigma=2, level_method=Levels.SCALE_SATURATED_BY_CHUNK)
glp = Filter.GaussianLowPass(sigma=1), in_place=True), in_place=True), in_place=True)

# scale data with user-defined factors to normalize images. For this data set, the scale factors
# are stored in experiment.json.
scale_factors = {
    (t[Axes.ROUND], t[Axes.CH]): t['scale_factor']
    for t in experiment.extras['scale_factors']
filtered_imgs = deepcopy(imgs)
for selector in imgs._iter_axes():
    data = filtered_imgs.get_slice(selector)[0]
    scaled = data / scale_factors[selector[Axes.ROUND.value], selector[Axes.CH.value]]
    filtered_imgs.set_slice(selector, scaled, [Axes.ZPLANE])

# Decode with PixelSpotDecoder
psd = DetectPixels.PixelSpotDecoder(
initial_spot_intensities, prop_results =
# Select only decoded spots that pass thresholds and map to genes in codebook
decoded = initial_spot_intensities.loc[initial_spot_intensities[Features.PASSES_THRESHOLDS]]
decoded_filtered = decoded[ != 'nan']

# Load cell mask
roi_path = os.path.join(os.path.dirname("__file__"), '')
masks = BinaryMaskCollection.from_fiji_roi_set(path_to_roi_set_zip=roi_path, original_image=dapi)

# Assign spots to cells by labeling each spot with cell_id
al = AssignTargets.Label()
labeled =, decoded_filtered)

# Filter out spots that are not located in any cell mask
labeled_filtered = labeled[labeled.cell_id != 'nan']

Now that every feature in the DecodedIntensityTable is labeled with a valid cell_id, the features can be grouped by cell into a single cell gene expression matrix. In this matrix, each row is a cell and each column is a gene. The values within the matrix are the number of features of that particular gene in that particular cell.

# Transform to expression matrix and show first 12 genes
mat = labeled_filtered.to_expression_matrix()
mat.to_pandas().iloc[:, 0:12].astype(int)

In addition to the matrix, ExpressionMatrix contains cell metadata, (e.g. cell location and cell size) stored as Coordinates of the matrix. When transforming a DecodedIntensityTable to an ExpressionMatrix, the initial cell metadata Coordinates are the location, number of undecoded spots, and area. The location is not based on the cell masks, but calculated from the central position of spots assigned to each cell. The number of undecoded spots is zero for each cell in this example because the undecoded spots were removed after decoding. The area, on the other hand, is always set to zero and needs to be calculated from the BinaryMaskCollection as shown below. New metadata fields can also be added.

# Add area (in pixels) of cell masks to expression matrix metadata
mat[Features.AREA] = (Features.CELLS, [ for _, mask in masks])

# Add eccentricity of cell masks to expression matrix metadata
from skimage.measure import regionprops
mat['ecc'] = (Features.CELLS, [regionprops(, coordinates='rc')[0].eccentricity for _, mask in masks])

# Show expression matrix with metadata

# Hierarchically cluster matrix and view as heatmap
import seaborn as sns
            xticklabels=['cell {}'.format(n + 1) for n in range(25)],

Finally, the ExpressionMatrix can be loaded into other analysis tools such as scanpy and Seurat by saving it as an AnnData, Loom, or NetCDF file.

Save as .netcdf for saving and loading in starfish pipeline'')

# Save as .h5ad file for loading in scanpy

# Save as .loom file for loading with loompy or loomR

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