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Python API Overview

The Celldega Python API provides modules for pre-processing spatial transcriptomics data, clustering analysis, neighborhood computation, and interactive visualization.

Installation

pip install celldega

Core Modules

Pre Module

The pre module contains functions for pre-processing raw spatial transcriptomics data into LandscapeFiles format. This includes:

  • Creating image tile pyramids for efficient zooming
  • Generating cell metadata and boundary tiles
  • Processing transcript data
  • Building cluster assignments and gene expression signatures
import celldega as dega

# Pre-process Xenium data
dega.pre.main(
    technology="Xenium",
    data_dir="/path/to/xenium_outs",
    path_landscape_files="/path/to/output",
    tile_size=250
)

Clust Module

The clust module provides the Matrix class for hierarchical clustering and heatmap visualization. It supports:

  • Automatic data processing pipelines
  • Multiple normalization methods (zscore, quantile, total)
  • Hierarchical clustering with dendrograms
  • Integration with Clustergram widget
import celldega as dega

# Create and cluster a matrix
mat = dega.clust.Matrix(adata, filter_genes=5000)
mat.cluster()

# Export for visualization
cgm = dega.viz.Clustergram(matrix=mat)

Nbhd Module

The nbhd module contains functions for computing and analyzing tissue neighborhoods:

  • Alpha shape computation for cluster-based neighborhoods
  • Hexagonal tiling for regular neighborhood grids
  • Neighborhood-by-gene expression analysis
  • Neighborhood overlap and bordering calculations
import celldega as dega

# Compute alpha shapes for cell clusters
gdf_alpha = dega.nbhd.alpha_shape_cell_clusters(
    adata, 
    cat="leiden", 
    alphas=[100, 150, 200]
)

# Generate hexagonal tiles
gdf_hex = dega.nbhd.generate_hextile(adata, diameter=100)

# Calculate neighborhood-by-gene expression
adata_nbg = dega.nbhd.calc_nbhd_by_gene(gdf_alpha, by="cell", adata=adata)

Viz Module

The viz module provides Jupyter Widget classes for interactive visualization:

Widget Description
Landscape Main spatial visualization for IST/SST data
Clustergram Hierarchical clustering heatmap
Yearbook Grid of cell "portraits"
Enrich Gene enrichment analysis 
import celldega as dega

# Create a Landscape widget
landscape = dega.viz.Landscape(
    base_url="https://your-landscape-files-url",
    adata=adata,
    ini_x=10000,
    ini_y=10000,
    ini_zoom=-5
)

# Display linked Landscape and Clustergram
display = dega.viz.landscape_clustergram(landscape, cgm)

Quick Reference

Common Imports

import celldega as dega

# Access submodules
dega.pre      # Pre-processing functions
dega.clust    # Clustering (Matrix class)
dega.nbhd     # Neighborhood analysis
dega.viz      # Visualization widgets

Key Classes and Functions

Module Key Components
dega.pre main(), create_image_tiles(), make_meta_gene()
dega.clust Matrix
dega.nbhd alpha_shape_cell_clusters(), generate_hextile(), calc_nbhd_by_gene(), NBHD
dega.viz Landscape, Clustergram, Yearbook, Enrich, landscape_clustergram()