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hutaobo/
ccst-spatial-clustering

Name: ccst-spatial-clustering
Availability: InStock

research

computational-biology

spatial-omics

graph-neural-networks

temporal-analysis

cc-by-nc-4.0

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COSTE + DST-GNN Manuscript Code

The repository path hutaobo/ccst-spatial-clustering is retained for continuity from an earlier private working name. The slug is historical; the public contents in this release are a cleaned DST-GNN implementation for the lung fibrosis manuscript workflow and are not a CCST release.

Overview

This repository contains a manuscript-aligned public implementation of the diffusion-based spatio-temporal graph neural network (DST-GNN) analysis described in:

Cophenetic Spatial Topology Embedding reveals multiscale tissue architecture in spatial omics

The implementation was reconstructed from the original analysis notebooks and cross-checked against the manuscript text. In particular, the public code follows the manuscript-level assumptions that:

the cohort contains 45 tissue samples grouped into three stages: T1 = HD, T2 = LA, T3 = MA
graphs are defined over 47 predefined cell types
edge values are COSTE/SSS-style spatial separation scores in [0, 1], where smaller values indicate closer proximity
missing cell-type pairs are treated as absent and filled with SSS = 1.0
node features are one-hot identity vectors
optimization uses Adam with an MSE objective
explainability is performed with PyTorch Geometric's GNNExplainer

What Is In This Repository

data/inputs/: bundled direct DST-GNN input CSV for the lung fibrosis cohort
data/outputs/formal_release/: bundled formal release outputs generated by the cleaned public pipeline
data/DATA_PROVENANCE.md: provenance notes for the bundled input and output data
src/dst_gnn/: cleaned Python package for data loading, temporal graph construction, model definition, training, and explainability
scripts/run_dst_gnn.py: end-to-end CLI that builds stage graphs, trains DST-GNN, ranks dynamic nodes and edges, and optionally runs GNNExplainer
scripts/verify_repro.py: output comparison helper for checking a rerun against the bundled formal release
references/RECOVERY_NOTES.md: provenance notes describing the recovered notebooks and the manuscript-alignment corrections applied here
upload_to_hf.py: helper for synchronizing this folder to the Hugging Face Hub

Bundled Data

The repository now includes the direct DST-GNN input table and a formal output release so users can rerun and verify the pipeline without searching for additional intermediate files.

Input:
- data/inputs/cophenetic_distances_searcher_D_score_in_all_samples.csv
Formal release outputs:
- data/outputs/formal_release/

The input CSV is the recovered cohort-level COSTE/SSS table with 45 samples, 47 cell types, and stage labels mapped as HD -> T1, LA -> T2, MA -> T3.

See data/DATA_PROVENANCE.md for the input lineage and the exact formal release settings.

Public Implementation Choices

The recovered notebook material contained exploratory and partially duplicated cells. This public release keeps the original analytical intent but makes several choices explicit:

Sample-level COSTE/SSS matrices are reconstructed into full 47 x 47 directed graphs per sample.
Missing pairs are filled with 1.0 before aggregation, matching the manuscript description of absent spatial associations.
Stage graphs are formed by averaging per-sample matrices within HD, LA, and MA.
Message passing uses an affinity transform 1 - SSS, so stronger spatial association yields stronger graph connectivity.
Temporal structure is modeled explicitly as the ordered sequence T1 -> T2 -> T3, with a GCN-based encoder, GRU-style temporal state update, and pairwise decoder trained to predict later-stage spatial relationships.

Expected Input

scripts/run_dst_gnn.py expects a CSV with these columns:

row
column
value
sample
group

This matches the recovered manuscript input table cophenetic_distances_searcher_D_score_in_all_samples.csv.

Usage

Install PyTorch and PyTorch Geometric with versions appropriate for your CPU/CUDA environment, then install the remaining dependencies:

pip install -r requirements.txt

Run the end-to-end analysis:

bash

python scripts/run_dst_gnn.py \
  --csv /path/to/cophenetic_distances_searcher_D_score_in_all_samples.csv \
  --output-dir outputs/lung_fibrosis_dst_gnn \
  --epochs 400 \
  --run-explainer

The script writes stage-level matrices, training history, predicted next-stage graphs, top changing nodes, top changing edges, and optional explainer outputs.

Reproducibility

The bundled formal release was generated with the cleaned public implementation using:

device=cpu
seed=0
hidden_channels=32
dropout=0.0
lr=0.01
weight_decay=5e-4
epochs=400
top_k=20
run_explainer=true

Reproduce the public release from the bundled input:

bash

python scripts/run_dst_gnn.py \
  --csv data/inputs/cophenetic_distances_searcher_D_score_in_all_samples.csv \
  --output-dir outputs/repro_check \
  --device cpu \
  --seed 0 \
  --hidden-channels 32 \
  --dropout 0.0 \
  --lr 0.01 \
  --weight-decay 5e-4 \
  --epochs 400 \
  --top-k 20 \
  --run-explainer

Verify the rerun against the bundled formal release:

bash

python scripts/verify_repro.py \
  --expected-dir data/outputs/formal_release \
  --actual-dir outputs/repro_check

The repository intentionally bundles the direct DST-GNN input and the formal output release, but it does not bundle the original raw Xenium data. Manuscript conclusions should still be cited to the associated paper.

Recovery Notes

The primary recovered sources were:

Y:/long/publication_datasets/Vannan_2023_Lung_Fibrosis/notebook/GNN modelling.ipynb
Y:/long/publication_datasets/Vannan_2023_Lung_Fibrosis/notebook/Expression Distance Similarity.ipynb

Mirrored copies were also located on the connected A100 server under /mnt/taobo.hu/long/publication_datasets/Vannan_2023_Lung_Fibrosis/.

See references/RECOVERY_NOTES.md for the full provenance summary.

Citation

Please cite the associated manuscript for biological findings and figure-level conclusions. The repository-level citation metadata is provided in CITATION.cff.

License

The currently published repository contents are distributed under a non-commercial license. See LICENSE.md for details.