Cell-GPS 0.0.6

Cell-GPS spatial topology analysis for spatial omics data.

Cell-GPS

Cell-GPS is the Python package and reference implementation for Cophenetic Spatial Topology Embedding (COSTE), a spatial topology analysis framework for spatial omics data. New Python code should import cellgps; the historical sfplot import namespace is retained for backward compatibility.

This repository is maintained as both the installable Python package and the code companion for the Cell-GPS/COSTE bioRxiv preprint.

Preprint and manuscript code

Cell-GPS/COSTE is described in the associated bioRxiv preprint:

Long M, Hu T, Sountoulidis A, Samakovlis C, Nilsson M. Cophenetic Spatial Topology Embedding reveals multiscale tissue architecture in spatial omics. bioRxiv. 2026. doi: 10.64898/2026.05.26.727847

Versioned bioRxiv page: https://www.biorxiv.org/content/10.64898/2026.05.26.727847v1

If you use the Python package, the Windows executable, the R companion package, or the manuscript figure/table code, please cite this preprint.

The code used to generate the preprint figures and supplementary tables is organized in Cell-GPS manuscript code/:

• main_figures/: notebooks for main figure analyses.
• supplementary_figures/: notebooks for supplementary figure analyses.
• supplementary_tables/: notebooks for supplementary table analyses.

The notebooks are intentionally output-free and preserve the original manuscript data paths where those paths were required for reproduction. A detailed mapping from manuscript results to source code is available in docs/cellgps_science_manuscript_code_inventory.md.

Package Names

• Python distribution: Cell-GPS
• Python import package: cellgps
• R package/repository: cellgpsr
• Windows executable: cellgps.exe

The Python package is hosted at https://github.com/hutaobo/cellgps. The R package is hosted separately at https://github.com/hutaobo/cellgpsr. The Windows single-file executable is distributed through Zenodo: https://zenodo.org/records/17859173.

What Cell-GPS does

• Computes searcher-findee distance matrices from spatial coordinates and cell labels.
• Builds cophenetic distance matrices and StructureMap heatmaps from AnnData objects or plain pandas tables.
• Loads 10x Xenium outputs and prepares them for downstream spatial analysis.
• Provides Xenium loaders backed by pyXenium.io, including standard Xenium folders and table bundles with cells.parquet, official *_cell_groups.csv, and cell_feature_matrix.h5.
• Supports transcript-by-cell analysis for locating transcripts relative to cell types.
• Includes memory-optimized workflows for large datasets.
• Provides plotting utilities such as clustered heatmaps, circular dendrograms, and related summary figures.

Repository layout

• src/cellgps/: recommended Python import namespace.
• src/sfplot/: legacy compatibility namespace and current implementation modules.
• tests/: package tests and smoke checks.
• docs/: Sphinx documentation.
• Cell-GPS manuscript code/: curated preprint figure and table notebooks.
• sfplot-manuscript/: legacy manuscript-specific notebooks, figures, and derived outputs.
• benchmarking/: benchmarking-related material.
• segmentation_methods/: supporting segmentation workflows.

Installation

Install from PyPI:

pip install Cell-GPS

Install from the maintained conda channel:

conda install -c https://raw.githubusercontent.com/hutaobo/conda-channel/main cell-gps

Install directly from GitHub:

pip install git+https://github.com/hutaobo/Cell-GPS.git

For local development or reviewer inspection:

git clone https://github.com/hutaobo/Cell-GPS.git
cd cellgps
pip install -e .

The package requires Python 3.9 or later.

Quick start from a coordinate table

The minimal input is a table with spatial coordinates and a cell-type column.

import pandas as pd
from cellgps import compute_cophenetic_distances_from_df, plot_cophenetic_heatmap

df = pd.DataFrame(
    {
        "x": [0, 1, 5, 6],
        "y": [0, 1, 5, 6],
        "celltype": ["A", "A", "B", "B"],
    }
)

row_coph, col_coph = compute_cophenetic_distances_from_df(
    df=df,
    x_col="x",
    y_col="y",
    celltype_col="celltype",
)

plot_cophenetic_heatmap(
    row_coph,
    matrix_name="row_coph",
    output_dir="output",
    output_filename="StructureMap_example.pdf",
    sample="Example",
)

Quick start from Xenium output

from cellgps import load_xenium_data, load_xenium_table_bundle, compute_cophenetic_distances_from_adata

# Standard Xenium folder through pyXenium.io
adata = load_xenium_data("/path/to/xenium/run", normalize=False)

# Explicit table-bundle route used for the Atera Xenium benchmark
adata = load_xenium_table_bundle("/path/to/xenium/run", normalize=False)

row_coph, col_coph = compute_cophenetic_distances_from_adata(
    adata,
    cluster_col="Cluster",
    output_dir="output",
)

Useful public entry points

• load_xenium_data: load and preprocess Xenium data.
• load_xenium_table_bundle: load Xenium data from cells.parquet + *_cell_groups.csv + cell_feature_matrix.h5 through pyXenium.io.
• compute_cophenetic_distances_from_df: compute structure matrices from a coordinate table.
• compute_weighted_searcher_findee_distance_matrix_from_df: weighted searcher-findee kernel for entity, pathway, or LR analysis.
• compute_weighted_cophenetic_distances_from_df: weighted StructureMap wrapper over the weighted kernel.
• compute_cophenetic_distances_from_adata: compute structure matrices from AnnData.
• compute_entity_to_cell_topology: generalize t_and_c from transcripts to arbitrary weighted entities.
• compute_entity_structuremap: build StructureMap-style topology among arbitrary weighted entities.
• plot_cophenetic_heatmap: generate StructureMap and related clustered heatmaps.
• transcript_by_cell_analysis: analyze transcript-to-cell spatial structure at scale.
• ligand_receptor_topology_analysis: score sender→receiver ligand-receptor candidates using topology, structure compatibility, and local contact.
• ligand_receptor_target_consistency: add a NicheNet-style downstream target-consistency layer.
• compute_pathway_activity_matrix: compute rank-based or weighted pathway activities per cell.
• pathway_topology_analysis: analyze pathway-to-cell and pathway-to-pathway spatial topology.
• compute_cophenetic_distances_from_df_memory_opt: memory-aware alternative for large tables.
• plot_circular_dendrogram_pycirclize: circular dendrogram visualization.

Notes for reviewers

• The recommended Python import namespace is cellgps; the legacy sfplot namespace remains available for older scripts.
• The curated figure and table notebooks for the bioRxiv preprint are kept in Cell-GPS manuscript code/.
• sfplot-manuscript/ is retained as a legacy manuscript workspace with additional notebooks, generated figure assets, and intermediate outputs.
• Raw experimental datasets are not bundled in this repository because of size and distribution constraints. The code expects standard spatial omics outputs such as Xenium folders or tabular coordinate inputs.
• When a cellgps_tbc_formal_wta/results-style directory is already available, the LR and pathway topology extensions are designed to reuse its t_and_c_result_*.csv and StructureMap_table_*.csv outputs as the preferred gene-level topology anchors before falling back to recomputation.
• Xenium loading depends on pyXenium>=0.4.3. Visium helpers remain optional through the separate Cell-GPS[visium] extra.
• A short repository walkthrough is available in REVIEWER_GUIDE.md.

Documentation

Sphinx documentation sources are available in docs/.

Citation

If you use Cell-GPS or reuse the manuscript analysis code, please cite:

Long M, Hu T, Sountoulidis A, Samakovlis C, Nilsson M. Cophenetic Spatial Topology Embedding reveals multiscale tissue architecture in spatial omics. bioRxiv. 2026. doi: 10.64898/2026.05.26.727847

@article{long2026cellgps,
  title = {Cophenetic Spatial Topology Embedding reveals multiscale tissue architecture in spatial omics},
  author = {Long, Mengping and Hu, Taobo and Sountoulidis, Alexandros and Samakovlis, Christos and Nilsson, Mats},
  journal = {bioRxiv},
  year = {2026},
  doi = {10.64898/2026.05.26.727847},
  url = {https://www.biorxiv.org/content/10.64898/2026.05.26.727847v1}
}

License

This project is released under the MIT License. See LICENSE.