2D Pipeline Tutorial

2D Pipeline Tutorial#

This tutorial demonstrates how to use InterSCellar for 2D spatial omics analysis.

Cell Neighbor Detection & Graph Construction#

The 2D pipeline starts with detecting cell neighbors based on surface distance:

import interscellar

neighbors_2d, adata, conn = interscellar.find_cell_neighbors_2d(
    polygon_json_path="data/cell_polygons.json",
    metadata_csv_path="data/cell_metadata.csv",
    max_distance_um=1.0,
    pixel_size_um=0.1085,
    n_jobs=4
)

Parameters#

  • polygon_json_path: Path to JSON file containing cell polygon coordinates

  • metadata_csv_path: Path to CSV file with cell metadata

  • max_distance_um: Maximum distance in micrometers for neighbor detection

  • pixel_size_um: Pixel size in micrometers

  • n_jobs: Number of parallel jobs

Output#

The function returns: * neighbors_2d: DataFrame with neighbor pairs * adata: AnnData object (if available) with graph information * conn: Database connection object

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