3D Pipeline Tutorial

3D Pipeline Tutorial#

This tutorial demonstrates how to use InterSCellar for 3D spatial omics analysis.

Step 1: Cell Neighbor Detection & Graph Construction#

First, detect cell neighbors in 3D space:

import interscellar

neighbors_3d, adata, conn = interscellar.find_cell_neighbors_3d(
    ome_zarr_path="data/segmentation.zarr",
    metadata_csv_path="data/cell_metadata.csv",
    max_distance_um=0.5,
    voxel_size_um=(0.56, 0.28, 0.28),
    n_jobs=4
)

Parameters#

  • ome_zarr_path: Path to OME-Zarr file containing 3D segmentation

  • metadata_csv_path: Path to CSV file with cell metadata

  • max_distance_um: Maximum distance in micrometers for neighbor detection

  • voxel_size_um: Tuple of (z, y, x) voxel sizes in micrometers

  • n_jobs: Number of parallel jobs

Step 2: Interscellar & Cell-Onl Volume Computation#

After detecting neighbors, compute the interscellar volumes:

volumes_3d, adata, conn = interscellar.compute_interscellar_volumes_3d(
    ome_zarr_path="data/segmentation.zarr",
    neighbor_pairs_csv="results/neighbors_3d.csv",
    neighbor_db_path="/results/neighbor_graph.db",
    voxel_size_um=(0.56, 0.28, 0.28),
    max_distance_um=3.0,
    intracellular_threshold_um=1.0,
    n_jobs=4
)

Parameters#

  • ome_zarr_path: Path to OME-Zarr file containing 3D segmentation

  • neighbor_pairs_csv: Path to CSV file with neighbor pairs from step 1

  • neighbor_db_path: Path to database file with neighbor pairs from step 1

  • voxel_size_um: Tuple of (z, y, x) voxel sizes in micrometers

  • max_distance_um: Maximum distance for volume computation

  • intracellular_threshold_um: Threshold for intracellular volume classification

  • n_jobs: Number of parallel jobs

Output#

The function returns: * volumes_3d: DataFrame with interscellar volume measurements * adata: AnnData object with volume information * conn: Database connection object

After computing interscellar volumes, you can compute cell-only volumes by subtracting the interscellar volumes from the original segmentation:

cellonly_3d = interscellar.compute_cell_only_volumes_3d(
    ome_zarr_path="data/segmentation.zarr",
    interscellar_volumes_zarr="results/interscellar_volumes.zarr"
)

Parameters#

  • ome_zarr_path: Path to original OME-Zarr file containing 3D segmentation

  • interscellar_volumes_zarr: Path to interscellar volumes zarr file from step 2

The output zarr file is automatically saved in the same directory as the interscellar_volumes_zarr.

Output#

The function returns: * cellonly_3d: DataFrame with cell-only volume measurements

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