revise-svc 0.0.32

Vision-integrated spatial transcriptomics SVC reconstruction and Sim2Real-ST benchmarking.

REVISE

REVISE (REconstruction via Vision-integrated Spatial Estimation) reconstructs Spatially-inferred Virtual Cells (SVCs) from spatial transcriptomics data by integrating ST measurements, histological images, and matched single-cell RNA-seq references.

The current codebase is organized around one configuration-driven engine, REVISEPipeline, and two user-facing modes:

Mode	Goal	Main entry points	Primary outputs
benchmark	Reproduce Sim2Real-ST evaluations across six confounding factors	benchmark_main.py, benchmark_main.sh, reproduce/benchmark/*.ipynb	metrics_normalized.csv with PCC, SSIM, MSE, and NRMSE
application	Reconstruct SVCs and run downstream real-data analysis	application_sp_SVC_recon.py, application_sc_SVC_recon.py, reproduce/case/*.ipynb	sp_SVC.h5ad, sc_SVC_expr.h5ad, sc_SVC_spatial.h5ad, notebook figures

Documentation: https://revise-svc.readthedocs.io/en/latest/

Dataset and reproduced results: https://zenodo.org/records/17705737

What REVISE Covers

Sim2Real-ST benchmarks six confounding factors across three spatial transcriptomics platform types:

• Spatially heterogeneous factors: image segmentation artifacts and bin-to-cell assignment errors.
• Spatially homogeneous factors: spot size, batch effect, gene panel limitation, and gene dropout.

REVISE reconstructs two complementary SVC types:

• sp-SVC: spatial refinement for hST platforms such as Visium HD.
• sc-SVC: molecular completion and cell-state refinement for iST/sST platforms such as Xenium and Visium.

Architecture

Modern runs flow through:

1. revise.framework.REVISEPipeline
2. revise/revise.yaml profiles and runtime/io overrides
3. revise.recon.pipeline.UnifiedReconstructionPipeline
4. backend strategy and plugin registries in revise/backend/

UnifiedReconstructionPipeline owns the fixed lifecycle: input validation, global anchoring, local unit preparation, graph construction, OT problem construction, OT solving, expression update, SVC finalization, and optional benchmark evaluation.

Legacy-style runner classes are kept under revise/backend/runners/ for notebook compatibility and parity checks. New code should prefer REVISEPipeline or the root wrapper scripts.

Installation

Install the package from PyPI:

pip install revise-svc

Optional annotation support:

pip install "revise-svc[annotation]"

Development install:

git clone https://github.com/wuys13/REVISE.git
cd REVISE
pip install -e ".[dev]"

Download Sim2Real-ST benchmark data and real application data from Zenodo, then place them under raw_data/ if you want to reproduce the paper results.

Quick Start

Benchmark Mode

benchmark_main.py runs Sim2Real-ST cases and writes per-gene benchmark metrics. The paper-facing metrics are PCC, SSIM, and MSE; NRMSE is also retained in the CSV for legacy compatibility.

python benchmark_main.py \
  --cf segmentation \
  --raw_data_path raw_data/Sim2Real-ST \
  --sample_name P2CRC/cut_part1 \
  --task segmentation \
  --save_path output/benchmark

Supported --cf values:

• segmentation
• bin2cell
• batch_effect
• spot_size
• gene_panel
• gene_dropout

Use the merged launcher for multi-case reproduction:

bash benchmark_main.sh

Application Mode

Application scripts default to output/ subdirectories so notebook analysis can load the reconstructed SVC files directly.

For hST / Visium HD style sp-SVC reconstruction:

python application_sp_SVC_recon.py \
  --raw_data_path raw_data/Real_application \
  --sample_name P1CRC \
  --st_file HD.h5ad \
  --sc_ref_file adata_sc_all_reanno.h5ad

Default published notebook output:

output/sp_SVC_case/<sample_name>/sp_SVC.h5ad

For iST / Xenium style sc-SVC reconstruction:

python application_sc_SVC_recon.py \
  --sample_name P2CRC \
  --data_type Xenium \
  --raw_data_path raw_data/Real_application \
  --sc_ref_file adata_sc_all_reanno.h5ad \
  --select_ct T

Default published notebook outputs:

output/sc_SVC_case/<sample_name>_<data_type>/<select_ct>/sc_SVC_expr.h5ad
output/sc_SVC_case/<sample_name>_<data_type>/<select_ct>/sc_SVC_spatial.h5ad

Python API

from revise.framework import REVISEPipeline

pipeline = REVISEPipeline(config_path="revise/revise.yaml")
svc = pipeline.run(
    profile="application_sc",
    runtime_overrides={"platform": "iST", "confounding": "segmentation"},
    io_overrides={
        "data_root": "raw_data/Real_application",
        "output_root": "output/sc_SVC_case",
        "sample_name": "P2CRC",
        "st_file": "Xenium.h5ad",
        "sc_ref_file": "adata_sc_all_reanno.h5ad",
        "patient_key": "Patient",
    },
    set_overrides=["sc.select_ct=T"],
)

Notebooks

Area	Files	Purpose
Benchmark	reproduce/benchmark/seg_benchmark.ipynb, spot_benchmark.ipynb, batch_benchmark.ipynb, imputation_benchmark.ipynb	Inspect Sim2Real-ST benchmark outputs and PCC/SSIM/MSE trends
Application reconstruction	reproduce/case/*_recon.ipynb, reproduce/case/sp_SVC_case.ipynb	Rebuild paper application cases from raw inputs
Application analysis	reproduce/case/*_analysis.ipynb, application_sc_SVC_analysis_case.ipynb	Analyze SVC outputs, cell states, pathways, spatial patterns, and downstream figures
SMI case	SMI/CosMx-SMI-REVISE_spSVC.ipynb	CosMx SMI sp-SVC application example

ReadTheDocs links the maintained benchmark and case notebooks through docs/benchmark/ and docs/case/.

Repository Layout

• revise/framework.py: public REVISEPipeline entry point.
• revise/revise.yaml: routing profiles and default configuration.
• revise/recon/: unified pipeline context and lifecycle orchestration.
• revise/backend/: strategies, platform adapters, plugin registries, kernels, and lower-level operations.
• revise/config/: config loader and internal runner configuration contracts.
• revise/analysis/: benchmark metric and downstream analysis helpers.
• reproduce/benchmark/: benchmark launchers and analysis notebooks.
• reproduce/case/: real application reconstruction and analysis notebooks.
• docs/: ReadTheDocs / Sphinx source.

License

REVISE is released under the MIT License.